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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
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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.

2,536 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 9 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.

afffundunlabeled
3D LAND COVER CLASSIFICATION BASED ON MULTISPECTRAL LIDAR POINT CLOUDS
Guihua Zhao, Jonathan Li, Yuanxi Yang, Yong Fang
2016· 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
40
citations
fundvenueaboutno affunlabeled
Quantification of landscape change from satellite remote sensing
Steven E. Franklin, Elizabeth Dickson, M. Hansen, Dan Farr, L. Monika Moskal
2000· article· en· The Forestry Chronicle· Environmental Science
distilled prediction:candidate · insufficient_payloadconsensus · insufficient_payload
40
citations
affunlabeled
AN EFFICIENT DEEP LEARNING APPROACH FOR GROUND POINT FILTERING IN AERIAL LASER SCANNING POINT CLOUDS
Abdul Nurunnabi, Felix Norman Teferle, Jonathan Li, Roderik Lindenbergh, Addisu Hunegnaw
2021· 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 · none
39
citations
affunlabeled
General sky models for illuminating terrains
Patrick J. Kennelly, A. James Stewart
2013· article· en· International Journal of Geographical Information Systems· Environmental Science
distilled prediction:candidate · noneconsensus · none
38
citations
afffundaboutunlabeled
UNMANNED AERIAL VEHICLES PRODUCE HIGH-RESOLUTION, SEASONALLY-RELEVANT IMAGERY FOR CLASSIFYING WETLAND VEGETATION
James V. Marcaccio, Chantel E. Markle, Patricia Chow‐Fraser
2015· 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
37
citations
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