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Yankees Urged To Go Big With CJ Abrams Blockbuster Deal
The New York Yankees shouldn't be afraid to make a splash this summer.
Apple’s Eddy Cue on ‘F1’ Sequel, Why Apple TV Has Never Licensed Any Library Content and Being Inspired by Steve Jobs
Apple hasn’t officially greenlit a sequel to last year’s blockbuster “F1: The Movie,” but Eddy Cue — who oversees the tech giant’s entertainment businesses — said one will “hopefully” be coming, in pa...
Ranking the top 10 NFL offseasons: Myles Garrett trade was a win-win for two teams on AFC heavy list
From blockbuster free agent signings to draft-day steals, we rank the NFL franchises that made the biggest roster upgrades.
'Busting stereotypes': How metal music could help mental health
The initiative sets out to challenge stigma and outdated stereotypes associated with heavy metal.
Senators trade All-Star Brady Tkachuk to Panthers in blockbuster for package that features 3 first-round picks, unites him with his brother
The Senators will receive three-first round picks and a second in exchange for dealing the All-Star winger in his prime.
Senators reportedly trading All-Star Brady Tkachuk to Panthers in blockbuster for package that features 3 first-round, unites him with his brother
The Senators will receive three-first round picks and a second in exchange for dealing the All-Star winger in his prime.
Senators reportedly trading All-Star Brady Tkachuk to Panthers in blockbuster for package that features 3 first-round, unites hit with his brother
The Senators will receive three-first round picks and a second in exchange for dealing the All-Star winger in his prime.
Why the owner of bustling Prince George’s Co. restaurant is seeking clarity about its entertainment license
A local restaurateur who says she is accused of violating her lease is now facing a new challenge.
Panthers Acquire Brady Tkachuk From Ottawa In Blockbuster Trade
2026 NBA Mock Draft: Giannis Antetokounmpo trade would create chaos; Wizards select Darryn Peterson at No. 1
There is bound to be drama on draft night and in this mock draft, we project trades that could happen -- including the Giannis blockbuster
3 team NBA blockbuster trade involving Giannis Antetokounmpo gaining momentum
The Giannis Antetokounmpo trade rumors are heating up once again, and the latest reports suggest the race for the two-time MVP may be taking another dramatic turn. For weeks, speculation surrounding Antetokounmpo’s future has dominated NBA headlines off the court.…
Seahawks' Rival Announces Parting Shot After Blockbuster Trade
It appears that despite not being in the division anymore, one Seattle rival still wants to inflict pain on the franchise.
Dinosaurs invaded downtown St. Paul for blockbuster 1986 Science Museum exhibit
The brontosaurus and a 10-foot-tall Tyrannosaurus rex had been floated down the Mississippi River from Minneapolis that morning on coal barges to drum up publicity for the exhibit. It worked.
What happened to the cast of “Jaws”? See the stars of the first summer blockbuster more than 50 years later
Celebrating more than half a century of quoting, "You're gonna need a bigger boat."
The Market’s AI Fanfare Is Running Into a Harsh Political Reality
Anthropic’s hopes for a blockbuster IPO could depend as much on the ballot box as on investors.
George Lucas comes out of retirement for voice role in upcoming Hollywood blockbuster
George Lucas is coming out of retirement to provide a voice role in the first Hollywood blockbuster to directly involve him in over a decade —and it's not a 'Star Wars' or 'Indiana Jones' project.
Dolphins urged to make notable trade before 2026 season
The Miami Dolphins were urged to make a blockbuster trade ahead of the 2026 NFL season. Here's what ESPN wants Miami to do.
Commentary: ‘Disclosure Day’ underscores a Hollywood marketing dilemma
From "Close Encounters of the Third Kind" to "E.T. the Extra-Terrestrial" and "War of the Worlds," a UFO-themed blockbuster from Steven Spielberg has long been a can’t-miss event, and last
Rap star Busta Rhymes headlining stacked Choctaw Indian Fair entertainment lineup
The Choctaw Indian Fair is rapidly approaching, and we now have a closer look at what all will be taking place during the event's 76th edition this summer.
Fantasy Football Sleepers, Busts & Predictions: 2026 Philadelphia Eagles
This year promises to be a big year of transition in Philadelphia. A.J. Brown is gone, and Makai Lemon, Dontayvion Wicks and Hollywood Brown are here now. Kevin Patullo and Jeff Stoutland are gone as offensive coordinator and offensive line…
Merchants of Doubt and the attacks on Extreme Event Attribution science
Last week, I attended a meeting at Columbia University on attribution science and climate law, hosted by the Sabin Center. It was a fantastic event, bringing together scientists and legal experts working at the intersection of extreme event attribution and climate law. Subscribe For those unfamiliar with it, extreme event attribution attempts to quantify the contribution of climate change to an extreme event. For example, several groups analyzed the impact of climate change on Hurricane Harvey’s enormous rainfall totals over Houston, Texas and they found that climate change increased rainfall by 15 to 38%. One thing that came up again and again was how terrified fossil-fuel interests are of extreme event attribution science. They are acutely aware that this research could land them in court. And losing those cases would leave them legally liable for billions of dollars in climate damages. Because the legal stakes are so high, the blowback has turned ugly. I spoke with several scientists at the meeting who are facing ongoing harassment over their work. This blowback is a coordinated campaign to make the entire field look suspect. The goal is to create the impression that attribution science is too uncertain, too political, or too conflicted to be useful in court or in public policy. The strategy is not based on actual science or evidence of misconduct, but on the generation of doubt. The new Merchants of Doubt We’ve seen this before. In fact, not that long ago: We only have to go back a year to the Department of Energy (DOE) Climate Working Group (CWG) report to see an example of using doubt as the tool to push back against well-established science. This strategy is laid out in an email from a member of the CWG, Dr. Roy Spencer, that was released during litigation over the Climate Working Group process. The key quote is: About all I can hope is that what we write will provide sufficient “reasonable scientific doubt” regarding the science claims in the 2009 TSD [technical support document], based upon almost 2 decades of new science, to call into question the original reasoning for the EPA Administrator’s decision that CO2 presents a threat to human health and welfare. This statement is strong evidence that at least some members of the committee were working to support a particular policy outcome: revoking the Endangerment Finding. The email also explains how they planned to do it: by attempting to generate “reasonable doubt”. This is going to be hard, Spencer implies. Despite falsely claiming that “2 decades of new science” weakens the case, Spencer explicitly acknowledges that the actual peer-reviewed science of climate change overwhelmingly rejects his position: But if the science argument is decided upon by a vote, or by the number of published citations, we lose the science argument. We can go back even further: This CWG email shares unmistakable DNA with the infamous 1969 tobacco memo that declared: “Doubt is our product, since it is the best means of competing with the ‘body of fact’ that exists in the mind of the general public. It is also the means of establishing a controversy.” The tobacco memo also acknowledges the limit of this strategy: Like the CWG, they knew the science was not on their side. Share The new new Merchants of Doubt The people attacking the IPCC chapter on extreme event attribution are the newest iteration of the Merchants of Doubt. Their goal, like all Merchants before them, is to introduce doubt into the process. Because the report is not even out yet, they cannot attack its conclusions. So they are attacking the authors instead. Here is a press release from the House Science, Space, and Technology Committee: In the letter, the Chairmen express concerns about potential conflicts of interest involving members of the Attribution Committee, stating that “publicly available information suggests a troubling pattern” in which committee members are affiliated with nonprofits that support climate accountability lawsuits, “raising the appearance of impropriety and member bias.” To be clear, this is just innuendo. There is no actual evidence of bias. And given the robust process that these reports go through, including multiple lines of peer review, it seems very unlikely that significant bias can survive into the report. When the report comes out, critics will have the opportunity to make legitimate criticisms of the report — if any exist. If none do, however, they’ll still make criticisms, but they’ll be bogus, simply designed to generate doubt. We’ll see. A note to the press: Fix your frame To any journalists reading this: The public debate over extreme event attribution science is not going away. The science is simply too dangerous to fossil-fuel interests for them to stop fighting it. You very well might be assigned to write an article about this area of research in the future. When you do, do not automatically adopt the framing that climate misinformers want you to use. They want you to frame the story around questions like: Are climate scientists trying to put their thumb on the scale to achieve a predetermined, politically motivated result? Are climate scientists improperly letting their politics invade the science of the IPCC? That frame is a trap. Instead, you need to view this through the historical lens of the Merchants of Doubt. How does the ecosystem of doubt operate? Who funds it? What methods do they use to misrepresent science and slime researchers? What scientific results are they trying to keep people from understanding are legitimate? Ultimately, you instead need to focus your article on the generation of doubt as a way to maintain the fossil fuel industry’s social and legal license to keep burning oil, gas, and coal. If you treat the misinformers’ frame as a legitimate, good-faith scientific critique, you are helping them produce doubt. Don’t do it. Don’t be a Merchant of Doubt. Thanks for reading The Climate Brink! Subscribe for free to receive new posts and support our work. Subscribe other stuff The Staying Curious Substack has an interesting post about the different flavors of climate sensitivity. This is crucial information if you want to understand how scientists think about how much warming the Earth will experience. I’d be grateful if you could hit the like button ❤️ below! It helps more people discover these ideas and lets me know what’s connecting with readers.
Dave & Buster's Entertainment, Inc. Stock 12‑Month Price Target Cut to $19.78, Implies 61% Upside
Explore more price target data and ratings for Dave & Buster's Entertainment, Inc. on the Forecasts tab, and track all previous and future analyst recommendations for Dave & Buster's Entertainment, Inc. in the dedicated News Flow.Select reference data provided by FactSet. Copyright © 2026 FactSet R…
For sports fans looking for a piece of peace, June is busting out all over
Something special is going on throughout North America. We’re stepping outside to join others to partly root, root, root for the home team.
Dave & Buster's Entertainment, Inc. Stock 12‑Month Price Target Cut to $20.11, Implies 63% Upside
Explore more price target data and ratings for Dave & Buster's Entertainment, Inc. on the Forecasts tab, and track all previous and future analyst recommendations for Dave & Buster's Entertainment, Inc. in the dedicated News Flow.Select reference data provided by FactSet. Copyright © 2026 FactSet R…
Jaguars Hunting Season 2026: The New England Patriots
After a meteoric rise to the Super Bowl, Mike Vrabel’s squad faces offseason turmoil and a blockbuster trade before heading to Jacksonville for a high-stakes AFC showdown.
What's the latest on Giannis and Jaylen? O'Connor offers insight
Kevin O'Connor of Yahoo Sports joins Early Edition to share his latest intel on a potential Giannis Antetokounmpo-to-Boston blockbuster.
Dave & Buster's Entertainment Q1 Earnings Call Highlights
Dave & Buster's Entertainment (NASDAQ:PLAY) reported weaker-than-expected first-quarter results for fiscal 2026, with management pointing to macroeconomic pressure in April, a softer consumer backdrop and promotional missteps as contributors to a 5.4% decline in comparable store sales. Chief Ex
Bronzeville Businesses Still Cleaning Up After Violent Storms, Water Main Break
BRONZEVILLE— Multiple Bronzeville businesses are still cleaning up from a series of severe storms last week, with fallout including a busted water main
Dave & Buster's Entertainment Inc. (PLAY) Stock Falls on Q1 2026 Earnings
Dave & Buster's Entertainment Inc. (PLAY) reported first-quarter 2026 results with total revenue of $559.2 million, a 1.5% decline year-over-year, and gross profit of $479.4 million, down 1.3% versus the prior year.Total revenue: $559.2 million (-1.5% YoY)Gross profit: $479.4 million (-1.3% YoY)Operating profit: $46.9 million (-25.8% YoY)Net income attributable to common shareholders: $5.7 million (-73.7% YoY)Diluted earnings per share: $0.16 (-7
Review | Spielberg’s return to science fiction, “Disclosure Day” is a bold, messy blockbuster
Steven Spielberg is one of the most prolific directors of all time, so obviously, when he has a new movie coming out, I get incredibly excited. Spielberg’s return to science fiction with “Disclosure Day” is in many ways just what I expected from him, but also was head-scratching in some areas that I thought it...
Under Trump, hopes for a mining boom in the Nevada desert
Tonopah has boomed and mostly busted since it's heyday more than a century ago. Trump has raised expectations for renewed prosperity and one local businessman is sprucing up his motel in anticipation of better times.
How SpaceX employees, new millionaires should spend their IPO windfall
SpaceX's blockbuster IPO will ink thousands of new millionaires. Wealth advisors urge them to spend smartly.
Paramount's $111-billion Warner Bros. acquisition clears key hurdle
As expected, the U.S. Justice Department gave its consent for the blockbuster merger expected to reshape Hollywood
Jamie Dimon hosts SpaceX IPO celebration at JPMorgan HQ
Jamie Dimon is rolling out the red carpet for SpaceX employees. Take a look inside JPMorgan's lavish plan to celebrate SpaceX's blockbuster IPO.
Spielberg seeks blockbuster comeback with ‘Disclosure Day’
Steven Spielberg began tackling the story for his latest movie, "Disclosure Day," after a rare sequence of two films that fell flat at the box office. He started writing in
Drake Maye says A.J. Brown gives Patriots missing element
New England Patriots quarterback Drake Maye is excited to play alongside star receiver A.J. Brown following the blockbuster trade.
Brandon Valenzuela Making Blue Jays' Tough Roster Decision Easy
When the Toronto Blue Jays traded busted prospect Will Wagner to the San Diego Padres last year in return for catcher Brandon Valenzuela, the team could never have expected the 25-year-old Mexican backstop to perform the way he has in his rookie season. Through the first 45 games of his MLB career, ...
Surprising things about visiting San Antonio, Texas, from New Englander
As a New Englander, I was surprised to find all that the Texas city of San Antonio had to offer, including bustling art, wine, and dining scenes.
POP Culture Group Reports Stellar Half-Year Results: Robust Business Growth with Revenue Surging 141% Year-Over-Year
During the reporting period, the Company recorded net revenue of $68,902,790, representing a year-over-year increase of 65% compared to $41,813,266 in the same period of 2024, marking a leapfrog expansion in business scale. Revenue from the digital entertainment segment increased from $37.18 million for the six months ended December 31, 2024 to $66.57...
OpenAI files confidential S-1, but says IPO 'may be a while'
OpenAI has filed its confidential S-1, the first official step on its path to a blockbuster IPO. The ChatGPT-maker announced the filing on Monday.
We can create the future of science right now
Paul Litvak is the founder and Executive Director of the Robyn Dawes Institute and a Visiting Scholar at UC Berkeley. He has a PhD in Behavioral Decision Research from Carnegie Mellon where he studied emotions and the sunk cost bias. Over 15 years in industry, he solved a wide range of challenging technical problems. At Meta he created models optimizing the ad review process. At Google he ran experiments to measure social influence. At Airbnb, he was a product manager leading machine learning teams optimizing search ranking and price suggestions. He also co-founded and led product at Rhythmic Health, a venture-backed biosensing startup, creating an accurate low cost system using a color changing strip and a smart phone to measure salivary lactate. The bottleneck At this point, it is uncontroversial to say that science needs to stop using the PDF article as the unit of knowledge and currency. The unbearable slowness of scientific publishing, the profit motive and margins: I’m not saying anything new. The PDF also sucks because it’s hard to extract structured information, which makes it hard to do evidence synthesis. As a result, we do much less evidence synthesis than is needed. And evidence synthesis ultimately undergirds most policy and medical decision-making. I can see second-by-second real-time odds for any sporting or newsworthy event, but a school board can’t see the best evidence on whether their 8th graders should be taught algebra. As a society, we don’t treat this as an important problem. Again, not controversial. Not only is the problem well understood, but the solution has already been laid out. What we need is AI-assisted living evidence synthesis - (1) an open knowledge graph of atomic claims, (2) claims linked to evidence, (3) assessment and synthesis of each piece of evidence, and (4) continuous updating with new data. What few realize (yet) is that the technical capacity to build this vision for a significant portion of science already exists. Not only that, scientists and startup teams are already building many of these components. I know this because I’ve been surveying the space and talking to many of the builders. There are some missing pieces: for example, evaluations of how well some of the components work. But at this point, most of what’s missing is a fully end-to-end working integration of all of these parts. In the rest of this essay, I’m going to lay out all the parts of a working living evidence layer for science and who is working on them, and propose concrete next steps for building this system. What’s now possible The diagram above outlines the components of a living evidence synthesis platform, including some of the teams working on each component1. Scientific PDFs are processed into claims with associated evidence. The evidence is subjected to a forensic audit, methodological evaluation and robustness and reproducibility checks. Finally it’s given a weight in a continuously updating synthesis. What follows is a description and status of each component and a few of the teams working on them. Document understanding The first thing you need to be able to do is turn an article into structured data. Mostly that means parsing PDFs. There are often multicolumn layouts that confuse non-specialized PDF to text processing libraries. For scientific papers, there is the added complexity of parsing formulas and tables and figures. This is a really hot area - there are startups offering APIs, and it seems like a new open source package gets posted to Github every few weeks. What follows isn’t exhaustive. A package called GROBID was the state of the art for a while, they didn’t update their package for nearly two years until very recently. In the meantime reducto.ai released an AI powered PDF extraction API, PaddleOCR became popular, IBM released a model called Docling, and both Mistral and Gemini created models and libraries. I also know of at least one other well-funded psychology research group working on a paper parser. By contrast, there are few open evals in this space, with no extensive evals for complex table comprehension in particular. Nonetheless, I’m confident this will be a solved problem soon, given the combination of LLM advances and developer interest. Hypothesis level extraction There has been increasing interest in comprehending the extracted text of papers and linking information to evidence for each hypothesis. A lot of work has already been done. Trialstreamer (Marshall et al. 2020) and RobotReviewer LIVE (Marshall et al. 2023) demonstrated automated extraction of trial population, intervention, and outcome at scale on clinical RCTs. PaperQA2 (Skarlinski et al. 2024) and Ai2 ScholarQA (2024) extended this to retrieval-augmented question answering with citation grounding. Elicit, Consensus, and SciSpace operationalized claim-level extraction for end users. OpenEval (Booeshaghi et al. 2026) is the most recent and most ambitious: 1.96 million atomic claims extracted from 16,087 eLife manuscripts using Claude Sonnet 4.5, grouped into ~299,000 results, with LLM evaluations showing 81% agreement with human peer review on a 2,487-paper subset. None of these solutions link claims to test-statistics, as you would need to evaluate randomized controlled trials. This is why I built the evidence.guide API - to extract hypotheses and associated test statistics from behavioral science papers. The best public eval of this kind of extraction I’m aware of comes from the recent SCORE project - they had humans code thousands of psychology papers to extract their claims by hand. It would be extremely helpful to the world if all scientific PDFs were available as structured open data. I’ve been working to make this happen, both directly at Berkeley and through coordination with large entities I can’t yet speak of; as hard as it is to do, I think it’s possible2. Forensic audit A lot of work has been done on forensic audit, but some gaps remain. Of course, for biology papers that rely on images for evidence, there are a variety of tools (notably Proofig and ImageTwin) to spot anomalies. These are still well short of what sleuths like Elizabeth Bik can do on her own, but these tools are constant companions among fraud analysts. There’s someone working on auditing Excel files for anomalies, and a number of teams are automating numerical checks like GRIM and SPRITE, including Scrutiny project, the INSPECT-SR team as well as statcheck. The regcheck team is building a way to use AI to compare preregistrations to analyses in papers, to ensure there aren’t significant deviations. Nonetheless, there are many other kinds of anomalies to screen for, both public and less publicly known. And there are no formal evals for anomaly detection that I’m aware of. Still, there’s a lot to draw from in this space and I’m pretty certain we will be able to scan papers for most kinds of obvious anomalies in the near future. Methodological review This area has been white hot, though I fear for many of the startups in this space, because this capability may become commoditized. There are at least six different AI peer review companies, including Refine.ink, Reviewer3, ReviewerZero.ai, Q.E.D. Science, Paper Wizard, and Isitcredible. Coarse (a pun on refine) was also recently created as an open source alternative. These systems provide qualitative feedback on the content of papers, spotting methodological weaknesses and mathematical errors. They seem to work pretty well, and many academics report bitterly that they exceed the average quality of typical peer reviewers. But there are few evals here either. What evals exist so far involve using LLM-as-judge (circularity problems abound) or comparing against human reviews of questionable quality. What you’d ideally want is an eval that measures capturing known errors in papers3. Reproducibility and robustness Another active area has been using AI agents to automate computational reproducibility4 and robustness5 checks in papers that report numerical results. For more recent papers where data and code are available, AI agents can see whether they can re-run the analyses and produce the numbers reported in the published paper. In addition to a handful of individual academics who have been experimenting using Claude Code for this, the Institute for Replication is a leading group working on building an end to end system. The evals related to this problem are the most mature, with CORE-Bench (Siegel, Kapoor, Narayanan 2024) and PaperBench (OpenAI 2025) available to benchmark agents on this task. There is also work on getting AI agents to test alternative ways of analyzing the data to ensure the results are robust to small analytic design choices. Synthesis This is the most underdeveloped area where significant investment is required. Although some automated evidence synthesis systems exist — for example, otto-sr is building an AI agent to write systematic reviews — none of these incorporate the full range of paper level signals to weight evidence appropriately. Nor is there anything like an eval or a gold standard for a good systematic review. Arguably Cochrane reviews are the closest we have to gold standard human systematic reviews, though I’ve heard academics in the know complain about their uneven quality. A key question for a synthesis platform is how to weight anomalies and methodological issues in assessing the quality of a piece of evidence. This is an unsolved problem and one I’m very keen to work on. Continuous updating There are many pieces of basic infrastructure available for monitoring for new research and initiating updates. OpenAlex is the current open citation graph. Retraction Watch integrated into Crossref in October 2023. Scite tracks how citations support, contrast, or mention prior claims. The Living Evidence Network demonstrated continuous-update workflows in clinical guidelines. Engineering this is a relatively straightforward task. When you look over this technical architecture and all the progress being made, it’s hard not to be optimistic that a living guide to scientific evidence will be built. The stakeholders are ready The social infrastructure for this is starting to coalesce — it’s not just a pie in the sky academic exercise to imagine this coming into existence. Institutions like the Center for Open Science, the Institute for Replication, the INSPECT-SR, the Living Evidence Network and more are all working on scaling work to improve research quality. Funders are also aligned. The Sloan Foundation has funded living evidence work through COS. Coefficient Giving supports the Institute for Replication and COS. The Astera Institute and the Institute for Progress have shown interest in this space. NIH has established an Office for Replication and Reproducibility. Although there are (very unfortunately) serious headwinds in science funding generally, there is an active group of funders interested in metascience. A brief word about what I’ve been doing at RDI. First, as a Visiting Scholar at Berkeley I’ve been actively figuring out how a non-profit and a public university can conduct and make public the results of large-scale academic article data mining. With some of the money I raised from donors, I commissioned a legal analysis of recent case law and publisher text data mining (TDM) agreements in order to understand whether a massive open data mining of academic articles is possible (with caveats, it is). I’ve also been working to bring together stakeholders in this space, and identify gaps. I’ve also been doing some software development in this space, with more to come. A pilot proposal The assumption undergirding all of this is that an AI, given all this information, would make the right judgment about a scientific claim with lots of conflicting evidence, weighing all the factors appropriately. That’s the hypothesis we need to test. Randomized control trial research is the best place to focus on first. RCTs are used to make many of the important decisions in society - from medical trials to public policy changes. And they use a relatively uniform set of inferential statistics with lots of known and available diagnostics. Behavioral science experiments, within the broader realm of RCTs, should be first used as a testbed whose results can be generalized. Because behavioral science is at the vanguard of open science practices, replications abound (there are thousands of them) to serve as ground truth training data. Key Hypothesis Therefore the pilot would test, in behavioral RCTs that have been replicated, whether the quality of evidence for a claim can be used to accurately predict whether that claim will replicate. Secondary Hypotheses Compared to claims that replicated, non-replicated claims demonstrate a greater share of forensic anomalies in their source literatures. Hypothesis level claim and statistic extraction is accurate enough to scale living evidence without onerous human review costs. Replication prediction is more accurate than prediction markets6 or journal prestige. If all the different quality signals we gather do accurately predict which studies will replicate, then we can use that model to score evidence to power the living evidence layer. Why this is informative regardless of outcome If the pilot succeeds, the architecture extends to medical RCTs (where Living Evidence already operates and integration is mostly about claim representation), then to slices of basic biology with stable replication structure. If it fails, the field learns which quality signals are load-bearing and which ones metascience has oversold. Either result is a contribution to knowing what the literature supports. Implications for funders Because this burgeoning ecosystem of builders already exists, a well-informed philanthropic or government funder could play a crucial catalyzing role in bringing this future about. They could play at least three roles: creating open structured datasets, publishing open benchmarks and incentivizing the solving of key technical challenges. First, open archives of papers that the government maintains, like PubMed, could be turned into structured data amenable to large scale metascience and claim aggregation. I know the US government already has an interest in doing this, though some key open questions remain unanswered. How do you determine the best models and systems for accurately extracting information from papers? How can you establish a robust way to allow researchers to flag errors and correct them? And finally, how do you create a legal regime in working with publishers to maximize the scope of available papers? For the latter, a university or a private philanthropy may be better positioned to make structured data publicly available under journal subscription terms or fair use. Philanthropists or government funders could also coordinate to create or commission benchmarks that evaluate whether important problems have been solved. For example, an open benchmark for claim extraction from a range of different scientific article types would be extremely helpful. Ensuring the underlying data are accurate is vital for creating these evaluations. I’ve discussed opportunistically using various human-created datasets for this purpose, but a consistent problem is that errors in human data make it difficult for them to serve as a gold standard. Finally, with structured data and benchmarks available, the government or private philanthropy could use them to incentivize groups to develop machine learning systems that meet these benchmarks. Prizes are one potentially valuable tool for this. For example, you could establish a prize for a system that accurately updates a living meta-analysis for a small set of claims. Prizes are particularly useful as signals of problem importance, and can help create vibrant ecosystems of public and private research—see, for example the role the government played in kickstarting the current work on nuclear fusion. Conclusion The drawbacks of the current scientific publishing system are known. Scientists agree, metascientists agree, philanthropists agree: the published PDF plus citation graph isn’t the right substrate for maintaining a representation of the evidence base in science. The pieces needed to build the alternative either already exist or are rapidly taking shape. The community is forming around exactly this problem, with concrete partnerships and shared infrastructure. A pilot should start on behavioral science RCTs because that’s the slice of empirical science most amenable to legibility, where replication ground truth is richest, and where the failure modes are best documented. What’s been missing is the galvanizing mission to assemble these pieces into something that works. That’s what I’m proposing to build. *** 1 I have a broader field map that I’ll release publicly soon. This is me, building in public! 2 If this is something that you are excited about, please reach out and talk to me. 3 More on this very soon too! 4 This tests whether, given the code and the data, you can get the same statistics as reported in the published paper. 5 This tests whether the results are the same as a paper’s given alternative analytical decisions in conducting the analysis (like outlier omission). Closely related is the idea of a “multiverse” where you come up with many different ways of answering the same underlying research question with the same data, and test whether the results hold in all those alternative methods. There’s been work on the latter as well. 6 Some of the replications, e.g. those from the SCORE project, had paired the experiments with forecasts from prediction markets. So we get to look at this for free.
Fantasy Football Sleepers, Busts & Predictions: 2026 Tampa Bay Buccaneers
Have the recent Buccaneers been a success? Over the last four years, the team has three division titles, and in the fourth, it tied for the division lead and lost out on a tiebreaker. On the other hand, that amounts…
Goldman Sachs pushes Fed rate-cut call to 2027 on strong US jobs data
Goldman Sachs expects the U.S. Federal Reserve to keep rates unchanged through 2026 and delay rate cuts until 2027, it said on Friday, citing stronger economic activity and job growth after a robust payrolls report.
Anthony Gordon Shares Marcus Rashford's Advice After Barcelona Transfer
Anthony Gordon has opened up about his blockbuster £70m move from Newcastle to Barcelona, revealing the private role Marcus Rashford played in the transition.
My son made me a soccer fan. Now, it's World Cup or bust.
I thought I hated sports, but my son made me fall in love with soccer. Now, I'm doing everything I can to get us to the World Cup.
Belmont Stakes Day Generates More Than $116 Million In Handle
Saturday's blockbuster Belmont Stakes Day program, highlighted by Golden Tempo’s victory in the 158th running of the $2-million Belmont Stakes (G1) presented by NYRA Bets, generated all-sources handle of $116,077,378 and paid attendance of 46,128. On-track handle for the 14-race Belmont Stakes Day ...
An Early Mock Trade To Give The Pirates’ Bullpen Help
The Pittsburgh Pirates’ bullpen is about as bad as one can get while not rebuilding. They’ve caused multiple late blown leads and heartbreaking disasters. The unfortunate reality is that no notable relievers are getting moved yet. ESPN’s Buster Olney even reported this. However, there might still be an opportunity for the Pirates to add someone. …
Business card found near school leads to Bridgeport brothel investigation
What started with a business card left on a parent's vehicle during school pickup has led to an investigation into an alleged brothel operating out of a home in Chicago's Bridgeport neighborhood.
Will Mike Evans Regret Leaving Buccaneers for 49ers?
A franchise icon departs Tampa Bay chasing a second ring, but a blockbuster trade by a division rival threatens to derail Mike Evans' championship plans in San Francisco.
Bills, Vikings Connected in Massive $140M Trade Rumor
A proposal has been made from one prominent NFL Analyst, who predicted a blockbuster Justin Jefferson trade rumor connecting the Bills, Vikings.
NOVUS vet: viral mitigation remains top priority for nursery pig health
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How ASUS Is Bringing AI into Healthcare Workflows
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How Science Works
The 2nd Weather and Climate Livestream is now underway. It is organized by a group of early-career researchers concerned about declining funding for the field and eager to demonstrate how weather and climate research makes a difference in our lives. Last year’s event was a great success, drawing terrific press coverage including a piece in the NY Times. This year features a strong lineup of talks aimed at a general audience — I highly recommend tuning in. Share I gave a talk yesterday about How Science Works in Three Steps. You can see the short presentation here (I’ve appended a transcript below): And please tune into the livestream here. It runs through tomorrow at 6 pm ET. Thanks for reading The Climate Brink! Subscribe for free to receive new posts and support our work. Subscribe Cleaned up transcript: Thank you, and thanks to everyone tuning in. I want to talk about something I think a lot of people don’t really understand: how science actually works. Arguments over any scientific topic — climate change, vaccines, you name it — revolve around competing claims. For climate change, the claims are: Is the Earth warming? Are humans to blame? What will the impacts be? Over the last 150 years, the scientific community has developed a remarkably robust method for sorting out which claims are true and which aren’t. That method is what I want to walk through. So how does science generate knowledge? If you ask a high schooler, they’ll give you the version they learned in ninth grade: an individual scientist forms a hypothesis, runs an experiment, and draws a conclusion. That’s more or less what happens at the individual level, though in practice it’s more iterative — you design, test, and refine all at once, landing on a slightly different idea each time until you reach a conclusion about whatever hypothesis you’ve ended up with. But this isn’t science. It’s only step one. Any individual scientist can make mistakes — scientists are human. Maybe there’s a bug in the code, maybe there’s an error in the experiment. Just because someone ran an experiment and reached a conclusion doesn’t mean it’s right, and scientists don’t treat a single result as settled truth. Step two is peer review, the first layer of quality control. The scientist writes up what they did and submits it to a journal, which sends the paper to other experts in the field. Those experts judge whether the methods are sound, whether it properly references prior work, and whether the conclusions actually follow from the data. Ask any scientist and they’ll tell you peer review is miserable — the comments come back brutal, often harsh, and you have to revise and resubmit, sometimes multiple times, before an editor finally accepts the paper. Peer review catches an immense amount of bad science, but it can’t catch all of it. Reviewers get sloppy or overworked, they miss obvious mistakes, and they can’t catch hidden errors — a bug in the code, a misread instrument. So a peer-reviewed paper making it into a journal doesn’t mean the paper is right. Nobody treats a single paper as the final word. That’s where step three comes in: the crucible of science. Important results get retested by the broader community. If you publish something interesting, it gets dissected. Sometimes people try to reproduce your work directly but more often they test its implications. If someone claims X is true, and that implies that Y is also true, then scientists will go check Y. If a claim survives this gauntlet long enough, it becomes accepted. That’s how we generate knowledge. Let me give an example: the ozone hole. In 1985 — and I doubt many people watching were around then, but I remember it well — Joe Farman and his colleagues published observations from Antarctica showing that a huge chunk of ozone was disappearing every year. This was shocking because most atmospheric chemists had expected ozone loss to occur at mid-latitudes and high altitudes, not over the South Pole. When scientists see something they don’t understand, they rush to explain it, and several competing hypotheses quickly appeared in the peer-reviewed literature. One pointed to chlorine chemistry from chlorofluorocarbons. A second blamed nitrogen chemistry, possibly driven by solar proton events — meaning the sun was responsible. A third argued it wasn’t chemistry at all but dynamics: winds were carrying naturally low-ozone air from the lower atmosphere up into the stratosphere. Then the community went to work testing which theory could survive the crucible. Aircraft flew into the ozone hole and found no nitrogen, ruling out the nitrogen theory. Measurements showed the air in the polar vortex was descending, not rising — which would bring high-ozone air down, eliminating the dynamics theory. Meanwhile, those same flights measured enormous amounts of chlorine, strongly correlated with exactly the regions where ozone was being destroyed. That became known as the “smoking gun” paper, and it established that chlorine chemistry was the cause. Through this process, a consensus emerged. And it’s worth being clear that this consensus is organic. There’s no vote, no poll, no meeting where it gets decided. Scientists go to conferences, talk to one another, and independently arrived at the same answer. It’s the ultimate free market of ideas. After the consensus was established, the peer-reviewed literature doesn’t even mention the rival theories — it simply takes chlorine chemistry as given. That’s what a consensus looks like. It doesn’t mean every single scientist agrees, but it does mean there’s no longer any legitimate debate. So where can you see what scientists actually think? It’s all in the peer-reviewed literature, which contains the entire historical record of the argument: the discovery, the competing theories, the debate, the testing, and finally the community coalescing around one answer. But there’s a lot of room for misrepresentation. You can dig up an old paper claiming the ozone hole was caused by dynamics and wave it around as if there still was a debate about the cause of the ozone hole — without mentioning that the idea was disproven long ago and that even its own authors no longer believe it. There’s a bigger problem, though: the peer-reviewed literature is written in a language I call “nerd.” It’s extremely hard to read — honestly, some of it is hard for me to follow. It takes graduate students years to work through the literature in a single field, so we can’t expect policymakers or ordinary people to do it. This is where scientific assessments come in. The most famous is the IPCC — the Intergovernmental Panel on Climate Change. The job of an assessment is to read and summarize the peer-reviewed literature, synthesizing what’s settled, what’s been disproven, and what remains genuinely debated. To ensure the assessments accurately summarize the peer-reviewed literature, assessments are written by large teams, which are far less prone to bias than individuals and which dilute the influence of any one scientist with an axe to grind. The teams are deliberately assembled to avoid stacking the deck, and the reports themselves go through multiple rounds of peer review. That rigorous process is exactly why you can trust them and why people consider the IPCC to be the gold standard of what we know about the climate system. An assessment produced through a different process may well not be credible. You can even watch the consensus evolve across assessments. Take the attribution of warming. The 1990 IPCC report said the observed warming was broadly consistent with model predictions but also comparable to natural variability — in other words, we don’t know whether humans are responsible. By 1995, it concluded that the balance of evidence suggested a discernible human influence — a sentence only a committee could write, and one that barely commits to anything. By 2001, most of the warming over the previous 50 years was judged “likely” due to greenhouse gases; by 2007, “very likely”; by 2013, “extremely likely.” The 2023 report says essentially the same as 2013 — there’s not much stronger language available. So, to wrap up: science is our ultimate tool for deciphering the universe — the most successful method we’ve ever developed for determining what’s true and what isn’t. Our understanding is written into the peer-reviewed literature, so if you want to know what scientists think, that’s where to look. And for those of us who don’t speak nerd, scientific assessments are the Rosetta Stone — they translate nerd into English. Let me open the floor to questions. Thank you.
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