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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.

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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 19 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.

afffundaboutunlabeled
Raster Vs. Point Cloud LiDAR Data Classification
Nagwa El-Ashmawy, Ahmed Shaker
2014· 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
14
citations
affunlabeled
INVESTIGATION OF POINTNET FOR SEMANTIC SEGMENTATION OF LARGE-SCALE OUTDOOR POINT CLOUDS
Abdul Nurunnabi, Felix Norman Teferle, Jonathan Li, Roderik Lindenbergh, Shahoriar Parvaz
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 · stsconsensus · none
14
citations
affunlabeled
A COMPARISON OF RANDOM FOREST AND LIGHT GRADIENT BOOSTING MACHINE FOR FOREST ABOVE-GROUND BIOMASS ESTIMATION USING A COMBINATION OF LANDSAT, ALOS PALSAR, AND AIRBORNE LIDAR DATA
Haifa Tamiminia, Bahram Salehi, Masoud Mahdianpari, Colin M. Beier, Lucas K. Johnson, Daniel B. Phoenix
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 · noneconsensus · none
14
citations
affunlabeled
Modelling vegetation understory cover using LiDAR metrics
Lisa Venier, Tom Swystun, Marc J. Mazerolle, David P. Kreutzweiser, Kerrie L. Wainio-Keizer, Ken A. McIlwrick +2 more
2019· preprint· en· bioRxiv (Cold Spring Harbor Laboratory)· Environmental Science
distilled prediction:candidate · metaepi_narrow+insufficient_payloadconsensus · none
14
citations
affunlabeled
Shape-preserving mesh decimation for 3D building modeling
Jing Li, Dong Chen, Fan Hu, Yuliang Wang, Peng Li, Jiju Peethambaran
2023· article· en· International Journal of Applied Earth Observation and Geoinformation· Environmental Science
distilled prediction:candidate · noneconsensus · none
13
citations
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