every number here carries its instrument
everything below is rendered straight from committed JSON files, the
same ones gaige export writes. statistics as measured, next to the
model, quantization, device, library versions, and corpus hash that produced them,
with the reproduce command riding along. nothing on this page is typed in by
hand.
HOW TO READ ONE
top of each card: AUROC, the bootstrap interval, the n_boot that produced it. then two threshold tables. the first is empirical, measured in-sample at target false-positive rates. the second is conformal: an order statistic of the human calibration scores, so the bound means something outside the sample too. some alpha rows refuse instead and name the sample size they would need. those refusal rows ship as-is; hiding them would be the dishonest version of this page.
nothing on this page transfers to your material. thresholds, scores, and separation are properties of one instrument on one corpus; the point of the export format is that you can re-run the command and check.
THE RECEIPTS
loading committed data from data/index.json…
VERIFY IT YOURSELF
$ pip install gaige # analysis core: numpy + requests $ gaige analyze --report reports/<yours> # replay any report, bit-identical, no GPU $ gaige export --report reports/<yours> --out site-data/ # emit this exact format
every card
links the raw JSON underneath it. inside each file there is a
reproduce block, and that command will rebuild the report on whatever
hardware you have. the numbers will come out different, because the instrument is
different, and that is the whole lesson.