If you look at the literature on this their argument was that some of the higher scoring samples weren't representative either (eg. 20% of kids not attending school). Schooling raises IQ scores. And the national IQs are not systematically lower than student achievement results so 🤷

Yeah, although that is something Lynn himself acknowledged. Iodine deficiency particularly iirc.

My point regarding GDP and health statistics is there are problems with that data from many countries too. Get better data. In terms of IQ itself it's one of the more robust measures in psychology. Lowered scores due to childhood lead exposure is a genuine concern. link.springer.com/article/10.1...

Sear is wrong. The dataset maps pretty well to international test scores and World Bank Harmonized Learning Outcomes. Issues arise with many countries in relation to GDP and health statistics too. Those who compiled it were quite open that it reflects environmental factors such as poor nutrition.

This should be very close to the final version, after a few improvements over the last few days.
@SenTomCotton @RandPaul @USRightToKnow
medium.com/@gillesdema...

You see also some of the most cited papers like Pekar et al (2022) supporting multiple spillovers appear to have serious flaws and may need to be retracted. @mbweissman.bsky.social arxiv.org/abs/2510.01484

Jane Qui has also written about the overconfidence of some on this topic. www.theguardian.com/commentisfre...

Ultimately, as Jesse Bloom has explained, the early case data is hopelessly biased. If it began in Wuhan WIV's sampling history in Yunnan and Laos provides a potential link. The NIH terminated WIV's subaward for refusal to share records of their SrCoV research. academic.oup.com/mbe/article/...

Wait- that's all stuff from Pekar 2022, the paper with enormous math errors that you just said was irrelevant. When those errors are fixed, its 2-spill probability becomes low, which undercuts its whole story. Tho P(other zoo stories) is unaffected by market-story errors.

By that token anything by anyone who cites anything by any of the 29 authors of Pekar et al. 2022 would have to be presumed false. Let's stick to the specific points. Here the only relevant one is whether leaving RE sites in eases making a series of partial new clones.

It was, in fact, a very good job by Brookings.
See michaelweissman.substack.com/p/an-inconve... for a (long) systematic summary of the evidence. For bite-size published refutations of the key zoonotic claims:
doi.org/10.1093/jrss...
doi.org/10.1093/jrss...
academic.oup.com/ve/article/1...

It's icky although the implications are hardly any worse than those for AI (mass unemployment, potential extinction).

Bloom again on evidence of bias in the early sequences and why the root of the SARS-CoV-2 tree cannot be conclusively resolved with the current data.
academic.oup.com/mbe/article/...

Who is conflicted in this scenario? There are obviously significant limitations with early case data which George Gao acknowledged was biased towards the market. The multiple spillover theory rests on a flawed analysis at odds with the analyses of Bloom, Kumar and Lv et al.
arxiv.org/abs/2510.01484

Very interesting. “one of the greatest lessons of the last five years is that public health needs to step up its game in dealing with uncertainty.”