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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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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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venuejournal
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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 22 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. 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.

affno abstractunlabeled
Recent Progress in Estimating Geoposition Using Daylight
David W. Welch, J. Paige Eveson
2001· book-chapter· en· Reviews: methods and technologies in fish biology and fisheries· Environmental Science
machine prediction:candidate · noneconsensus · none
10
citations
affunlabeled
POLE-LIKE OBJECT EXTRACTION FROM MOBILE LIDAR DATA
Han Zheng, Feitong Tan, Ruisheng Wang
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
machine prediction:candidate · noneconsensus · none
9
citations
affunlabeled
POLE-LIKE OBJECT EXTRACTION FROM MOBILE LIDAR DATA
Han Zheng, Feitong Tan, Ruisheng Wang
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
machine prediction:candidate · noneconsensus · none
9
citations
affvenueunlabeled
Spatial stratification in forest modelling
A. M. M. Nurullah, Glen A Jordan, Emin Zeki Başkent
2000· article· en· The Forestry Chronicle· Environmental Science
machine prediction:candidate · noneconsensus · none
9
citations
afffundunlabeled
WATER MAPPING USING MULTISPECTRAL AIRBORNE LIDAR DATA
Wai Yeung Yan, Ahmed Shaker, P. E. LaRocque
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
machine prediction:candidate · noneconsensus · none
9
citations
afffundaboutunlabeled
A ROBUST REGISTRATION ALGORITHM FOR POINT CLOUDS FROM UAV IMAGES FOR CHANGE DETECTION
Abdulla Al-Rawabdeh, Hussein Al-Gurrani, Kaleel Al-Durgham, Ivan Detchev, Fangning He, Naser El‐Sheimy +1 more
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
machine prediction:candidate · noneconsensus · none
9
citations
afffundunlabeled
DEEP MULTI-TASK LEARNING FOR TREE GENERA CLASSIFICATION
Connie Ko, Jian Kang, Gunho Sohn
2018· article· en· ISPRS annals of the photogrammetry, remote sensing and spatial information sciences· Environmental Science
machine prediction:candidate · noneconsensus · none
9
citations
venueno affno abstractunlabeled
10.1016/s0967-0653(97)87477-7
2000· article· en· Time to knit· Environmental Science
machine prediction:candidate · insufficient_payloadconsensus · insufficient_payload
9
citations
aboutno affunlabeled
Bathymetry of Lake Ontario
2018· dataset· en· Environmental Science
machine prediction:candidate · noneconsensus · none
9
citations

How this was built: Screen · Findings · About