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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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Forest Management and Policy
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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
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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.

2,661 results · 1 filter active ·
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20002025
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Machine labels · sparse coverage
Evidence
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An unlabeled work is unknown, not a negative. Label coverage is reported on every query.
2,661 works in the cohort · of 4,299,418page 44 of 54

Labels cover 7 of 2,661 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 2,661 of 2,661 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.

aboutno affno abstractunlabeled
Canada calls wood expert
2002· article· en· Materials Today· Environmental Science
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations
aboutno affunlabeled
Should I Manage a Forest?
2007· book-chapter· en· Environmental Science
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
Modeling bilateral forest products trade.
Craig Johnston, Brad Stennes, G. Cornelis van Kooten
2020· book-chapter· en· CABI eBooks· Environmental Science
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affno abstractunlabeled
World Vision Canada Poll: November 2005
2005· other· en· Roper Center for Public Opinion Research iPOLL· Environmental Science
machine prediction:candidate · noneconsensus · none
0
citations
afffundvenueaboutunlabeled
Challenges in teaching silviculture in Canada: the path forward
Amy Wotherspoon, Guillaume Moreau, Alexandre Morin-Bernard, Alexis Achim, Nicholas C. Coops, Olivier Villemaire‐Côté +11 more
2025· article· en· Canadian Journal of Forest Research· Environmental Science
machine prediction:candidate · noneconsensus · none
0
citations
venueno affunlabeled
PERSPECTIVE
2001· article· en· The Forestry Chronicle· Environmental Science
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations
aboutno affunlabeled
West Fraser Timber Co., Ltd.
2005· article· en· Mergent s Dividend Achievers· Environmental Science
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations
affno abstractunlabeled
Human Dimensions
Bob Tjaden, Marissa Jo Daniel, Francisco X. Aguilar, Hank Stelzer, Timothy Gallaher, John D. Kushla +32 more
2011· article· en· Journal of Forestry· Environmental Science
machine prediction:candidate · noneconsensus · none
0
citations
venueno affno abstractunlabeled
Model Forest News
2002· article· en· The Forestry Chronicle· Environmental Science
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations
aboutno affunlabeled
Neoseiulus californicus
2020· article· en· Zenodo (CERN European Organization for Nuclear Research)· Environmental Science
machine prediction:candidate · noneconsensus · none
0
citations
affaboutunlabeled
Forests
Sally N. Aitken
2020· book-chapter· en· Open Book Publishers· Environmental Science
machine prediction:candidate · noneconsensus · none
0
citations
venueaboutno affunlabeled
EDITORIAL / ÉDITORIAL
2004· article· fr· The Forestry Chronicle· Environmental Science
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
Universal Forest Products Inc.
2006· article· en· Mergent s Dividend Achievers· Environmental Science
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations

How this was built: Screen · Findings · About