The goal was to estimate the relative citation rank of each claim, combining citations to the preprint and the publication, as determined by Google Scholar one year from original upload. This is in general a completely intractable combinatorial problem, so we treated each preprint independently. Ideally closing prices are pretty evenly distributed from 0..100. Hard to evaluate that here, but you can see how papers “negotiated” their position.
Note the large number of claims that were reduced near 0% because they are already published, but then have last-minute “hockey stick” jumps back to about 50%. Strange. These appear to have been done by cthietje83, with typical single trades 1% ➛ 45%. But why buy so many surely losing shares? Yes they’re cheap, but for good reason! Theories:
Had we a realtime sparkline dashboard, or similar, it might have been easier for other traders to cooperate to correct in time.
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This research is supported (in part) by the Fetzer Franklin Fund of the John E. Fetzer Memorial Trust. It uses a platform developed for DARPA SCORE, and some staff are supported by SCORE while working on this. We are grateful for their support.