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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.

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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Species Distribution and Climate Change
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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.

7,879 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.
7,879 works in the cohort · of 4,299,418page 16 of 158

Labels cover 27 of 7,879 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 7,879 of 7,879 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
A metric‐based framework for climate‐smart conservation planning
Kristine Camille V. Buenafe, Daniel C. Dunn, Jason D. Everett, Isaac Brito‐Morales, David S. Schoeman, Jeffrey O. Hanson +5 more
2023· article· en· Ecological Applications· Environmental Science
machine prediction:candidate · noneconsensus · none
51
citations
fundno affunlabeled
Improved abundance prediction from presence–absence data
Erin Conlisk, John Conlisk, Brian J. Enquist, Jill Thompson, John Harte
2008· article· en· Global Ecology and Biogeography· Environmental Science
machine prediction:candidate · noneconsensus · none
51
citations
affno abstractunlabeled
Breaking the Habit(at)
Nicholas C. Coops, Michael A. Wulder
2019· article· en· Trends in Ecology & Evolution· Environmental Science
machine prediction:candidate · noneconsensus · none
50
citations
fundno affunlabeled
Modelling the impact of climate change on Tanzanian forests
Elikana John, Peter Bunting, Osian Roberts, Richard A. Giliba, Dos Santos Silayo
2020· article· en· Diversity and Distributions· Environmental Science
machine prediction:candidate · noneconsensus · none
49
citations
affunlabeled
Chemical Mixtures and Multiple Stressors: Same but Different?
Ralf B. Schäfer, Michelle Jackson, Noël P. D. Juvigny‐Khenafou, Stephen E. Osakpolor, Leo Posthuma, Anke Schneeweiss +2 more
2023· article· en· Environmental Toxicology and Chemistry· Environmental Science
machine prediction:candidate · noneconsensus · none
49
citations
aboutno affno abstractunlabeled
Frogs of the United States and Canada
Robert B. Powell
2013· article· en· Reptiles & Amphibians· Environmental Science
machine prediction:candidate · noneconsensus · none
49
citations
affunlabeled
Predicting range shifts of African apes under global change scenarios
Joana S. Carvalho, Bruce Graham, Gaëlle Bocksberger, Fiona Maisels, Elizabeth A. Williamson, Serge A. Wich +45 more
2020· preprint· en· bioRxiv (Cold Spring Harbor Laboratory)· Environmental Science
machine prediction:candidate · noneconsensus · none
48
citations
affunlabeled
Transdisciplinary science for improved conservation outcomes
Chris Margules, Agni Klintuni Boedhihartono, James Douglas Langston, Rebecca Anne Riggs, Dwi Amalia Sari, Sahotra Sarkar +3 more
2020· article· en· Environmental Conservation· Environmental Science
machine prediction:candidate · noneconsensus · none
47
citations

How this was built: Screen · Findings · About