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4,299,418 works, Canadian by any of four routes.

Every filter state is a URL; the URL is the query; the query is citable via /q/⟨hash⟩. The page, the API and the export parse the same parameters.

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The current cohort, streamed from the database: every work column, the machine labels, the provisional scores, and the per-row validation status. Exports are capped at 100,000 rows. Mints a permanent /q/ link for this exact query. The same filters always produce the same link, whoever asks.

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Direct Codex and Gemma labels are unvalidated and sparse. Distilled predictions cover the full frame and are also unvalidated. Choose the evidence source explicitly; absence of a direct label is never a negative label.

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The four routes compose: require the funder route and exclude affiliation to get the funder-only stratum no affiliation-based frame ever sees.

1,935 results · 1 filter active ·
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20002025
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Machine labels · sparse coverage
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An unlabeled work is unknown, not a negative. Label coverage is reported on every query.
1,935 works in the cohort · of 4,299,418page 12 of 39

Labels cover 22 of 1,935 works in this cohort. The rest are unlabeled, which is not a negative label: the label table is sparse today and grows as labeling rounds land.

Distilled predictions cover 1,935 of 1,935 works in this cohort. Predictions are machine_predicted_unvalidated. The Gemma side is a direct model label for every work (title-only); the Codex side is a distilled, calibrated classifier. Candidate is the union; consensus is the intersection.

afffundunlabeled
Hard thresholding regression
Qiang Sun, Bai Jiang, Hongtu Zhu, Joseph G. Ibrahim
2018· article· en· Scandinavian Journal of Statistics· Mathematics
machine prediction:candidate · noneconsensus · none
11
citations
affaboutunlabeled
Regularized Bayesian quantile regression
Salah‐Eddine El Adlouni, Garba Salaou, André St‐Hilaire
2017· article· en· Communications in Statistics - Simulation and Computation· Mathematics
machine prediction:candidate · noneconsensus · none
11
citations
affunlabeled
Universal Boosting Variational Inference
Trevor Campbell, Xinglong Li
2019· preprint· en· arXiv (Cornell University)· Mathematics
machine prediction:candidate · noneconsensus · none
11
citations
afffundunlabeled
Covariance regression with random forests
Cansu Alakuş, Denis Larocque, Aurélie Labbe
2023· article· en· BMC Bioinformatics· Mathematics
machine prediction:candidate · noneconsensus · none
10
citations
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How this was built: Screen · Findings · About