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Arizona political candidate says fear delayed report of alleged sexual assault
Bernadette Greene-Placentia says she’s sharing her story now in hopes that it could help others.
Joy Behar health update revealed alongside expected ‘View’ return date (exclusive)
EW has learned an update on Joy Behar’s health and when she’s expected to return to ‘The View’ after the cohost
Kakao Entertainment plots K-Pop growth with Atlantic Music Group
K-Pop isn’t exactly struggling to reach global listeners at the moment, through streaming and stadium tours alike. But now South Korean firm Kakao Entertainment has inked a deal with WMG’s Atlantic Music Group to continue the momentum.
1.2K views · 22 reactions | Nobel Prize 2026 explained: the...
Nobel Prize 2026 explained: the breakthrough that lets scientists switch brain cells on and off with light. Known as optogenetics, this technology helps researchers map the brain and uncover the...
'Digger's box office bomb is Tom Cruise's worst opening in 19 years
Tom Cruise's latest is on track to be this year's biggest box office bomb.
HHS awards nearly $250M in addiction treatment, overdose prevention grants
The U.S. | Projects supported include those to strengthen mental health support in schools, expand trauma treatment for families and children and more.
Downtown Gulfport business owners, Mainstreet bury hot dogs in hopes of clear skies for Cruisin’ the Coast
When they found out about an old wives tale of burying a hot dog to deter bad weather, those involved figured they would give it a try.
Why I invite the far left and right to my college debates
My peers deserved to engage with their views. What else is college for?
The Literacy Crisis Reaches Dog-Bear Science
“New analysis of more than 300 bear encounters suggests our canine companions often increase odds of conflict and rarely serve as early warning systems many recreationists assume,” reads a subhead in Mountain Journal. The thing is, that study reaches no such conclusion. But you have to read beyond the abstract to understand that. Mountain Journal is not alone. Let’s look at a brief list of takeaways published elsewhere just to establish that none of them seem to know how to read science. Wes Siler’s Newsletter is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber. Subscribe Here’s CBC: “A new peer-reviewed study, The Canine Conundrum: Is a Dog a Help or Hindrance in Bear Country?, dug into conflicts similar to what the video depicts, by analyzing 326 reported human-bear conflicts from 1901 to 2023 involving dogs.” “The study found 54 per cent of conflicts were started by dogs. In the encounters researchers included in the study, five people and 56 dogs were killed, and 170 people were injured.” “Lana Ciarniello, an independent researcher who co-authored the study, said researchers found when a person goes hiking with at least one other person, the odds of a negative bear encounter decrease by 50 per cent.” “However, when a dog is involved, the chances of a negative bear encounter increase because ‘a dog really adds a chaotic factor into the mix of recreation,’ said Ciarniello.” Again, the study finds no data supporting that conclusion. “Hiking with your pet dog may make you feel safer in bear country—but a hiker’s recent encounter and a new study prove that the numbers don’t add up,” writes Backpacker, which is now part of Outside. The numbers don’t add up to support this take. There’s plenty more examples, but all are equally dumb, so I’ll stop there. How did so many people get this so wrong? Three reasons: 1) the study is published behind a paywall, with only the abstract freely available. 2) the methodology sets out to create work relevant to a hyper-specific audience, not the public. And 3) the dataset studied is almost absurdly limited and doesn’t bear (get it?!) comparison to wider information. Let’s work through all three. The Problem is Money I understand the irony, since I’m writing about this on a subscription-model newsletter, but information wants to be free. The study is published by The Wildlife Society and costs $16 to access. Reprinting it is specifically prohibited by the terms of use, so I can only share snippets and my conclusions with you here. Having worked as a journalist for 24 years, I can report with some authority that journalists are both lazy and cheap. I don’t think it’s a stretch to say that much of the material you’re seeing written about this study is drawn from the freely-available abstract (above) only, and not a deep dive into its methodology or data. And that’s a problem, because abstracts are where researchers are able to break from their numbers and editorialize their opinions about conclusions. So what’s getting reported isn’t based on numbers, it’s based on an opinion designed for a specific audience, not the general public. Hell yeah ladies, kick that bear’s ass. The Methodology is Designed to Support a Predetermined Conclusion One of the biggest issues with the science that everyone likes to cite about bear spray’s efficacy is that no one understands that study was designed for a specific audience, looking for a specific answer. And since that audience is not the public, and our safety was not the question being asked, its conclusion is rendered irrelevant to our needs. Yes, I’m talking about Efficacy of Bear Deterrent Spray in Alaska, which was created by Tom Smith and Stephen Herrero, two of the same scientists who put together this dog-bear conflict analysis. In the spray study, the purpose was to present relevant agencies with the information they needed to issue their field workers with bear spray. As such, incidents where people were unable to access the spray or similarly failed to get it deployed in time were excluded from the results, and only incidents they deemed successful were included. Could a ranger roll down their vehicle’s window and scare a bear away from a dumpster by spraying it? Turns out the answer was yes, and that was good enough for the purposes of that study. The sample size of actual bear attacks—the thing you and I would want bear spray to provide a solution to, was too small to draw conclusions from, and didn’t indicate success in that role. Is a Dog a Help or Hindrance in Bear Country? has similar limitations. “Insights provided here are intended to help wildlife managers create effective guidelines regarding the presence of domestic dogs in bear habitat,” is explained clearly by the study’s text. To conduct the study, Smith and Herrero went looking for publicly available reports of dog-bear conflicts: “All incidents included in our analysis involved at least one bear, one dog, and one person who was at immediate risk of injury (e.g., not in a protective structure such as a cabin or vehicle), injured, or killed,” explains the report’s methodology. Why is that a problem? “We recognize that data used in this study are biased in that people generally only report adverse interactions with bears, not benign ones. Consequently, we assume that for the most part, people and their dogs pass through bear country without incident.” As was the problem with the bear spray study, people are drawing conclusions here from a study that was never intended to be relevant to the general public. The Smith-Herrero database famously excludes Alaska’s Defense of Life and Property records, which are elsewhere considered the largest and most authoritative source on human-bear conflict. The Dataset is Absurdly Limited A 2009 study conducted by the Washington Department of Fish & Wildlife estimates that each year there are at least 43,000 documented complaints of human-bear conflict across North America. Is a Dog a Help or Hindrance in Bear Country? Examined those reports for years ranging from 1901 to 2023, and only managed to find 326 such incidents that met the criteria explained above. Let’s do some quick math, poorly. 43,000 incidents x 122 years = 5,246,000 total incidents of documented human-bear conflict. Smith and Herrero were only able to find 326 incidents that met their criteria for dog+human+bear+risk or occurrence of injury. That means there’s a 0.006 percent chance that the study’s conclusion—that dogs make bear encounters more dangerous—even gets a chance of occurring. And remember, that’s only in documented conflicts. There’s no way to know how many people across North America run into a bear in any given year without any conflict, but it’s probably safe to assume that the total number is well in excess of 43,000. And, dig deeper into the study and you can find further restrictions that skew its conclusions even more. “While the people involved in these incidents did not report the level of training their dogs had received, it is our assumption that most were untrained companion dogs as opposed to well-trained bear dogs, such as Karelians and livestock guardian breeds that have proven to be significant aids in bear country,” the study’s methodology explains. And that’s a problem, because an ongoing study here in Montana is finding that the simple presence of a Kangal like our youngest dog Teddy can reduce visits to private property from grizzly bears by 87.8 percent. In addition to being a badass, Teddy is also a companion animal. And in my admittedly limited sample size of the three dogs currently present in our living room, she’s no more effective than her mutt brothers. Plenty of people own a dog capable of deterring bears, even if they didn’t set out to own a bear dog. The real purpose of this study appears to be a recommendation that leashes are mandated and recreationists pursue training in their deterrent of choice. But that doesn’t make for sensational headlines. Are There Any Real Takeaways Here? Let’s throw some numbers around, just to try and get our hands around what the larger picture might look like for bears and dogs here. The American Veterinary Medical Association estimates that 42.6 percent of American households own a dog. The Canadian Animal Health Institute says roughly 35 percent of households in Canuckistan own a dog. In an attempt to create the worst possible result for dogs, let’s run with that 35 percent number. And given that outdoorsy and rural people are more likely to both own a dog, and have bear encounters, let’s just make a giant assumption and apply that number to those 43,000 conflicts documented annually. If 35 percent of 43,000 human-bear encounters involve a dog, then we can assume that at least 15,050 dog-bear conflicts are occurring annually in North America. 15,050 x 122 = 1,836,100. Smith and Herrero were only able to find 326 incidents that met their human, dog, chance or occurrence of injury criteria across 122 years. And only 170 documented human injuries. So, with all the obvious caveats about these numbers in no way describing reality beyond narrow study parameters, what they actually found is that your risk of injury if your dog tangles with a bear is only 0.009 percent. I’ll take those odds next time I’m hiking my dogs in bear country. Top photo: “Superintendent Horace Albright, eating pancakes with bears, at Lake Lodge, Yellowstone National Park.” NPS. A journalist with more than two decades of experience working around the world, Wes Siler is here to cut through the outrage and disinformation to bring you the factual, insightful, actionable reporting you need to understand what’s going on. Upgrading to a paid subscription supports this reporting, and buys personal access to Wes, who will help you save money on gear, plan outdoor adventures, and prepare for real life, and who promises he’s less salty in real life than he sometimes comes across as on the Internet. Wes Siler’s Newsletter is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber. Subscribe
Penobscot Community Health Care faces cash crisis, possible bankruptcy
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Melody Friedenthal explores new worlds in science fiction book club
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This chaplain may become first trans person elected to Michigan legislature
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Google Password Manager Attacks Could Let Malware Hijack Passkey-Protected Accounts
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Microsoft Says New Cybersecurity AI Model Helps MDASH Hit 95.95% at Half the Cost
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SC behavioral health agency cuts 47 jobs in administrative overhaul, workers say they had no warning
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The Tech Sell-off Goes Global
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Museum in Tewksbury offers journey through public health history
Nursing, mental health, dentistry and the fight against infectious disease highlight the Public Health Museum at Tewksbury Hospital.
Museum in Tewksbury offers journey through public health history
Nursing, mental health, dentistry and the fight against infectious disease highlight the Public Health Museum at Tewksbury Hospital.
Sky Sports: Liverpool star set to leave this summer after contract U-turn
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Yankees Cam Schlittler joins elite company in MLB history
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G.O.P. Proposes $1 Billion in Immigration Bill for Trump’s Ballroom Project
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NFL Draft 2026 results: Every pick from Rounds 1, 2 and 3
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“AI polls” are fake polls
A few weeks after Donald Trump’s second presidential win, I took the train up from London (where I was living at the time) to Oxford to attend a conference on polls and forecasts of the 2024 election. Most of the attendees were pollsters or academics, but I also watched presentations from Aaru and Electric Twin, two companies that do what is interchangeably called synthetic sampling, silicon sampling, or synthetic audiences. Stripped of startup jargon, that means they use large language models (LLMs) to simulate responses to public opinion polls by having AI agents take on the role of survey respondents. I had already heard of Aaru thanks to some articles with eye-catching headlines like “No people, no problem: AI chatbots predict elections better than humans” in the months leading up to Election Day. The founders were making some big, some might even say far-fetched claims, such as: “within two years, we will simulate the entire globe — from the way crops are grown in Ukraine to how that impacts production of oil in Iraq, trade through the strait of Malacca, and elections for the mayor of Baltimore.” When Semafor asked Aaru’s cofounders — Cameron Fink and Ned Koh — about my boss, they said “we respect all those who came before us.” Nate (as he so often does) shared his thoughts on Twitter: Fink and Koh were relatively good-natured about this back-and-forth when we spoke at Oxford. They even offered to mail me one of the t-shirts featuring Nate’s quote they apparently had made. I never took them up on the offer, which I now somewhat regret. These synthetic sampling companies fell off my radar for a while, but they do still exist. In fact, Aaru recently received a $1 billion valuation. Is what they’re doing anywhere close to the most important frontier in AI development? Not by a longshot, especially when Anthropic just developed a model so adept at exploiting software vulnerabilities that it’s only being released to 40 companies. Still, silicon sampling is increasingly finding its way into public polling. Axios reported in March that “a majority of people trust their own doctors and nurses” based on findings from Aaru — without mentioning that the “people” in that sentence were actually LLMs. Around the same time, the Public Sentiment Institute “boosted” their online sample of 373 real survey respondents with 114 AI agents.1 (Spoiler alert: even the co-founder of Electric Twin doesn’t think that’s a particularly defensible approach.) Polling companies like Qualtrics and Ipsos are also developing synthetic data panels. So, what should we make of these … “polls”? Let’s get one thing out of the way: whatever they are, they’re not polls in the way that term is usually defined. Subscribe You can’t replace polls with AI On one hand, using LLMs to essentially make up fake survey respondents sort of sounds like the dumbest idea ever, one that will at best imperfectly replicate real polls while introducing all sorts of biases. On the other hand, with LLMs improving at a remarkable, perhaps even alarming rate, maybe that means I’m a dinosaur at the ripe old age of 24 because I still want to rely on polls that talk to actual people. I’m not going to argue that synthetic samples are completely useless. In fact, as I’ll return to later, there is evidence that some techniques can replicate topline survey results quickly and cheaply. But the marketing from certain companies can be slightly optimistic. “No traditional poll will exist by the time the next general election occurs,” said Fink in 2024. We’re just 206 days away from the midterms, and based on the fact that I still have to collect a bunch of polls every day, I’d say he should have run that prediction by a sample of AI agents before the interview.2 To see why synthetic samples can’t replace polls, here’s a quick primer on how they work. The simplest version of these models involves taking a LLM (like ChatGPT or Claude), giving it a demographic profile (e.g., a white, college-educated woman who lives in Utah and makes $70k a year), and then asking it to respond to a survey question. You repeat that process a few thousand times using different demographic profiles and end up with a sample of synthetic survey responses. The actual models used by private companies are more sophisticated than this, usually because they incorporate more hypothesized demographic characteristics for each agent and provide them with extra information. Aaru, for example, feeds agents a diet of news and information they’d be likely to consume, while Electric Twin incorporates their customers’ proprietary data about the audience they’re trying to replicate. The way Ben Warner, the co-founder of Electric Twin, explained it to me was “we have a large amount of data on […] for instance, 5,000 people. Can we make an accurate prediction of how they would respond to another question?” Still, it should be obvious why synthetic samples can’t replace polls. Polling is fundamentally a data collection process. We might use surveys to make predictions by feeding them into election forecasts, but the main purpose of a poll isn’t prediction, it’s gathering new data about what people think and how they feel. Silicon sampling, on the other hand, produces no new data. It’s simply a model: you input LLM training data, demographic prompts, and a bunch of other information, and it spits out a prediction for what a poll would say. We love models here, but models aren’t polls. That difference is an important philosophical sticking point for most pollsters I talk to. “I think politics should stay away from [synthetic sampling], because we’re trying to […] represent the voice of the people,” said Natalie Jackson, a vice president at GQR Insights. Democratic pollster John Hagner told me: “I think I’m just incredibly skeptical of this idea. I don’t think it’s research. At that point, you’re asking the machine to tell you what you already believe.” Hagner has seen some presentations of early synthetic sampling experiments, but so far, “if it’s being used in a campaign, people are keeping it incredibly quiet.”3 But Eli, I hear you saying, aren’t polls themselves increasingly governed by modeling decisions? Indeed they are: pollsters’ choices on which sampling method to use, how to define their likely voter models, and how to weight their samples can and do lead to dramatic differences in the results they publish. Aaru even referenced these limitations in the methodology statement included with that maternal mortality “poll” — although I’m using the term “methodology statement” loosely here, because it doesn’t really explain how the model works at all. We can ignore the (frankly preposterous) implication that synthetic sampling isn’t subject to a separate set of biases. The important point is that there’s still a meaningful difference between using weighting and other statistical techniques on actual polling data and using a model to predict what a poll would say. The latter is far closer to election forecasts or techniques like MRP — potentially useful models, but not a replacement for polls.4 To be fair, other synthetic sampling companies are perfectly happy with the distinction between polls and models. Warner compared polling and synthetic sampling to different tools in a toolbox. “The mistake I think we make is we think that these new tools should either work in exactly the same way or somehow replace these old tools,” he said. “Rather than thinking of it as, okay, so we’ve always had the hammer, we’ve always had the screwdriver, now we’ve got a saw. But don’t use a saw to try [to] do the job of a hammer.” A quick comment from Nate Eli didn’t ask me for a comment — rather rude of him, don’t you think? But since I’m editing this story, I figured I’d add a few quick thoughts rather than putting words in his mouth. Beyond the frequently misleading marketing, what bothers me about the AI “poll” hype is that as AI tools make statistical inference cheaper and/or better (note that these are not synonyms) that actually increases the comparative value of collecting original data. You might be able to train a model to make a reasonable estimate of what some hard-to-reach poll respondent would say — say, a young Black man who voted for Trump. (Such a person checks a number of boxes for a voter who is usually hard to reach in surveys.) Indeed, this is closely related to what models like the Silver Bulletin forecast already do. They essentially smooth out the kinks in noisy survey data by making inferences based on past voting patterns or national polls or surveys of other states. But you don’t actually know what these voters think unless you’re reaching them directly. If there’s a shift in opinion among this subgroup, you’re not going to detect it. So if I were running a campaign, I’d invest more in going the extra mile to find a representative sample of those voters. And then I’d hire some smart quants — or Claude? — to figure out the implications for campaign strategy based on proprietary data that my competitors didn’t have access to. -Nate Silver Are these models any good? If synthetic surveys are just a new type of model, the next obvious question is whether the models are at least accurate. The answer very much depends on who you ask. On one end of the spectrum, you have the maximalist argument that synthetic sampling is more accurate than actual polls. “It’s an incredibly challenging problem to go to someone and say ‘hey, we’re going to be more accurate at predicting human behavior than you, even when you talk to your customers directly’,” Koh recently told CNBC. In his view, synthetic sampling isn’t a saw to polling’s hammer, it’s “magic.” There’s certainly evidence that synthetic samples can replicate certain survey toplines. But if Aaru does have any examples of their approach outperforming the polls, they’re keeping those to themselves.5 Aaru’s 2024 election model, for example, had Kamala Harris leading in Michigan, Nevada, Pennsylvania, and Wisconsin on November 4th. And although they’ve since taken down their forecast page, they gave Harris a 50.5 percent chance of winning the race on November 2nd.6 After the election, Fink told Semafor he was happy enough with those results because they were “within margin of error,” a term that is completely meaningless when applied to a “sample” of AI agents. And of course, Aaru says their models have improved since 2024, so supposedly now they’d be more accurate than the polls? Still, their stronger argument is on cost: “We are significantly faster and cheaper than traditional polling, and still more accurate,” said Fink. The first two claims are undeniably true, but the third brings us to the opposite end of the spectrum. Both Jackson and Hagner are skeptical that these models are reliable for anything beyond replicating common survey toplines. “I just […] don’t think the machines are what we want when we’re looking for nuanced views. My example on this is people in Arizona and Nevada in 2024 who voted for Trump and voted for expanding abortion in their states on ballot initiatives,” said Jackson. Hagner identified another issue. Maybe the synthetic respondents, like sycophantic LLMs, are inhuman in one important way: they’re too nice. “The reports that have come through at the meetings that I’ve been at are that the early experiments on this, they cannot get respondents to be as racist or sexist or, frankly, as negative as human respondents,” he said. Academic research mostly agrees on this point. While there are some papers that show promising results when using LLMs to replicate polling data, most show that LLMs suffer from various quirks like producing too few “don’t know” responses and can seriously overpredict the favorability of politicians like Donald Trump and Kamala Harris. They also seem to struggle with too little variation between demographic subgroups, so the difference in predicted opinion between Democrats and Republicans, for example, is too small. When I asked Warner about these studies, his response to these papers was that just because academics can’t get synthetic sampling to work doesn’t mean that the technique doesn’t work in general. “Actually, the argument is, okay, yours does not [work]. That does not mean […] for this complex set of machinery, which uses a lot of investment, a lot of time, a lot of money, you can’t get it to work.” Cards on the table, I’m somewhat sympathetic to this argument because academics aren’t exactly great at making election forecasts. Usually, the people with skin in the game are the most accurate. Warner’s argument is that the approach Electric Twin takes — which includes, for example, making multiple predictions for each synthetic respondent using different models and prompts and subsequently averaging those to get a final prediction in a sort of ensemble forecast — produces better results than the simpler academic models. Warner shared a comparison between his method and the method from a recent academic paper with me, and Electric Twin was indeed able to get more accurate replication. But even still, he acknowledged that synthetic sampling “is not a crystal ball.” “If you asked me, do I think using other data sources will be more accurate than asking somebody who they will vote for, I would probably say no. But if you asked me ‘would your system be useful for our turnout modeling today?’ I would say yes.” For better or worse, it looks like the method is already getting more popular in the market research world. Most of the clients Aaru touts these days are businesses like EY and McDonald’s. And AI will probably begin to pop up in other parts of the political polling process. Pollsters are already using it to code open-ended survey responses, and some firms, like YouGov, are testing using LLMs to ask survey respondents questions. More worryingly, one danger to actual polls is that AI agents can be used to infiltrate online surveys. Most online polls use various checks to prevent that from happening, but there’s conflicting evidence on how effective those filters are and how prevalent AI agents currently are in online panels. If those agents ever become impossible to detect, it might spell the end of online polling, but the solution isn’t to replace all of your respondents with ChatGPT. Silver Bulletin is a reader-supported publication. To receive new posts and support our work, consider becoming a subscriber. Subscribe 1 That particular poll obviously doesn’t meet Silver Bulletin standards for aggregation. But we exclude all Public Sentiment Institute polls from our averages because we classify them as an amateur polling firm. 2 You could argue that Fink meant the next presidential election, but (a) I’m also confident we’ll still have real polls in 2028 and (b) in that case he should have asked an LLM to define “general election.” 3 Quick caveat: that’s reporting from a Democratic pollster. It’s possible that Republicans are more willing to use AI in political campaigns. 4 Indeed, Silver Bulletin does not include “polls” produced by MRP in our forecasts or averages, and we think it’s extremely misleading when their practitioners describe them in a way that suggests original data had been collected among a large number of states or Congressional districts. 5 A recent report from Aaru and EY did show two examples of a synthetic estimate being closer than a survey to a real-world benchmark — but I’d take those findings with a grain of salt because the report reads more like an ad and didn’t involve any sort of prediction being made ahead of time. 6 For comparison, our odds for Harris on the same day were 48.2 percent.
GlassWorm Campaign Uses Zig Dropper to Infect Multiple Developer IDEs
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AdventHealth takes first steps toward new sports medicine and rehabilitation space in Lake Placid
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Radio, TV, newspaper, digital: Women in regional media
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Rams to sign 2-time Super Bowl champion long snapper
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Los Angeles Rams make major decision on their future
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WMG’s Paul Robinson Accepts Entertainment Law Initiative Award: ‘Absolutely Thrilled’
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AI and the Entertainment Industry
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