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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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Remote Sensing and LiDAR Applications
Retraction
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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.

2,536 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.
2,536 works in the cohort · of 4,299,418page 12 of 51

Labels cover 3 of 2,536 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 2,536 of 2,536 works in this cohort. Predictions are machine_predicted_unvalidated teacher distillation outputs. Candidate is the union; consensus is the intersection.

afffundno abstractunlabeled
Digital Forestry: A White Paper
Guang Zhao, Guofan Shao, Keith M. Reynolds, Michael C. Wimberly, T. T. Warner, John W. Moser +12 more
2005· article· en· Journal of Forestry· Environmental Science
distilled prediction:candidate · noneconsensus · none
28
citations
affunlabeled
METHODS FROM INFORMATION EXTRACTION FROM LIDAR INTENSITY DATA AND MULTISPECTRAL LIDAR TECHNOLOGY
Marco Scaioni, Bernhard Höfle, Ana Paula Kersting, Luigi Barazzetti, M. Previtali, Daniel Wujanz
2018· article· en· ˜The œinternational archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences· Environmental Science
distilled prediction:candidate · metaepi_narrow+stsconsensus · sts
28
citations
affunlabeled
Slope Estimation from ICESat/GLAS
Craig Mahoney, Natascha Kljun, S. O. Los, L. Chasmer, Jörg Hacker, Christopher Hopkinson +3 more
2014· article· en· Remote Sensing· Environmental Science
distilled prediction:candidate · insufficient_payloadconsensus · none
27
citations
fundno affunlabeled
AERIAL POINT CLOUD CLASSIFICATION WITH DEEP LEARNING AND MACHINE LEARNING ALGORITHMS
E. Özdemir, Fabio Remondino, Alessandro Golkar
2019· article· en· ˜The œinternational archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences· Environmental Science
distilled prediction:candidate · metaepi_narrow+stsconsensus · sts
27
citations
fundaboutno affunlabeled
CLASSIFICATION OF AERIAL POINT CLOUDS WITH DEEP LEARNING
E. Özdemir, Fabio Remondino
2019· article· en· ˜The œinternational archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences· Environmental Science
distilled prediction:candidate · stsconsensus · none
27
citations
venueno affunlabeled
Forest ecotone survey by line intersect sampling
Piermaria Corona, Gherardo Chirici, Davide Travaglini
2004· article· en· Canadian Journal of Forest Research· Environmental Science
distilled prediction:candidate · noneconsensus · none
27
citations
affunlabeled
The use of airborne lidar for orchard tree inventory
Jae-Cheol Jang, Véronique Payan, Alain A. Viau, Alain Devost
2008· article· en· International Journal of Remote Sensing· Environmental Science
distilled prediction:candidate · noneconsensus · none
26
citations
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