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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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Label status

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
Evidence
Language
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Citations
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 42 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.

affvenueunlabeled
Les sursauts radio rapides
2018· article· fr· NPARC· Environmental Science
machine prediction:candidate · noneconsensus · none
0
citations
venueaboutno affunlabeled
Grand Banks iceberg management
2007· report· en· NPARC· Environmental Science
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
LiDAR Accuracy on North American Mountain Summits
Eric Gilbertson, R. Hensley, Andrew Kirmse, Kyle Bretherton, Kathryn Stanchak
2025· preprint· ArXiv.org· Environmental Science
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Modeling spaceborne lidar returns from vegetation canopies
Baoxin Hu, Iouri Tcherniavski, Alexander E. Dudelzak, Alexander Koujelev
2007· article· en· Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE· Environmental Science
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
Occurrence Download
2020· dataset· en· Global Biodiversity Information Facility· Environmental Science
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations
aboutno affno abstractunlabeled
Canadian Forest Photo Point Monitoring
John E Gregurke
2013· article· en· ˜The œBallarat naturalist.· Environmental Science
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
An Image Terrain Map Model for Texture Filtering
Yiyao Fan, Jun Lin, Changming Sun, Tianhao Wang, Yuehan Qi, Guanyu Zhang +1 more
2024· article· en· IEEE Transactions on Circuits and Systems for Video Technology· Environmental Science
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
Occurrence Download
2023· dataset· en· Global Biodiversity Information Facility· Environmental Science
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations
aboutno affunlabeled
Occurrence Download
2022· dataset· en· Global Biodiversity Information Facility· Environmental Science
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations
venueno affno abstractunlabeled
Full Issue in PDF / Numéro complet enform PDF
2006· article· fr· Canadian Journal of Remote Sensing· Environmental Science
machine prediction:candidate · insufficient_payloadconsensus · insufficient_payload
0
citations
aboutno affunlabeled
CONSIDERATIONS ABOUT MATHEMATICS
2021· article· en· CyberLeninK (CyberLeninka)· Environmental Science
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
Occurrence Download
2023· dataset· en· Global Biodiversity Information Facility· Environmental Science
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations
aboutno affunlabeled
Occurrence Download
2025· dataset· Global Biodiversity Information Facility· Environmental Science
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations
venueno affno abstractunlabeled
10.1016/s0967-0653(98)83321-8
2000· article· en· Time to knit· Environmental Science
machine prediction:candidate · insufficient_payloadconsensus · insufficient_payload
0
citations
affRetractionunlabeled
RETRACTED: A deep learning approach based on graphs to detect plantation lines
Diogo Nunes Gonçalves, José Marcato, Mauro dos Santos de Arruda, Vanessa Jordão Marcato Fernandes, Ana Paula Marques Ramos, Danielle Elis Garcia Furuya +7 more
2024· article· en· Heliyon· Environmental Science
machine prediction:candidate · research_integrityconsensus · none
0
citations
aboutno affunlabeled
cv femme de menage pdf
2024· other· fr· Zenodo (CERN European Organization for Nuclear Research)· Environmental Science
machine prediction:candidate · insufficient_payloadconsensus · insufficient_payload
0
citations
affunlabeled
The use of terrestrial LiDAR for enhanced forest inventory
Richard Fournier, Jean-François Côté, Joris Ravaglia, Ulysse Rémillard, Olivier Martin‐Ducup, Florentin Bourge +6 more
2017· preprint· en· HAL (Le Centre pour la Communication Scientifique Directe)· Environmental Science
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
INDIVIDUAL TREE IDENTIFICATION IN AIRBORNE LASER DATA BY INVERSE SEARCH WINDOW
Eric Bastos Görgens, Luiz Carlos Estraviz Rodriguez, André Luiz da; Universidade de São Paulo. Escola Politecnica Silva
2016· article· en· LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas)· Environmental Science
machine prediction:candidate · noneconsensus · none
0
citations
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