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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.

affaffiliation
fundfunder
venuejournal
aboutaboutness

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 51 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.

venueno affunlabeled
Qualisys Track Manager: User Manual
2004· other· en· NPARC· Environmental Science
machine prediction:candidate · insufficient_payloadconsensus · none
3
citations
afffundaboutunlabeled
Smart Cities : an IoT-centric approach
2014· article· en· Lancaster EPrints (Lancaster University)· Environmental Science
machine prediction:candidate · noneconsensus · none
3
citations
affno abstractunlabeled
Comprehensive Risk Assessment
Robert Waller
2009· book-chapter· en· NATO science for peace and security series. C, Environmental security· Environmental Science
machine prediction:candidate · noneconsensus · none
3
citations
affno abstractunlabeled
Response to Whittaker: challenges in testing for gender bias
Amber E Budden, Lonnie W. Aarssen, Julia Koricheva, Roosa Leimu, Christopher J. Lortie, Tom Tregenza
2008· article· en· Trends in Ecology & Evolution· Environmental Science
machine prediction:candidate · noneconsensus · none
2
citations
afffundunlabeled
A climate-smart spatial planning framework
Kristine Camille V. Buenafe, Daniel C. Dunn, Jason D. Everett, Isaac Brito‐Morales, David S. Schoeman, Jeffrey O. Hanson +5 more
2022· preprint· en· Research Square· Environmental Science
machine prediction:candidate · noneconsensus · none
2
citations
afffundunlabeled
The dominance–diversity dilemma in animal conservation biology
Charles A. Martin, Christopher J. Watson, Arthur de Grandpré, Louis Desrochers, Lucas Deschamps, Matteo Giacomazzo +6 more
2023· article· en· PLoS ONE· Environmental Science
machine prediction:candidate · noneconsensus · none
2
citations
affunlabeled
European scenarios for future biological invasions
Cristian Pérez‐Granados, Bernd Lenzner, Marina Golivets, Wolf‐Christian Saul, Jonathan M. Jeschke, Franz Essl +27 more
2022· preprint· en· bioRxiv (Cold Spring Harbor Laboratory)· Environmental Science
machine prediction:candidate · noneconsensus · none
2
citations

How this was built: Screen · Findings · About