MétaCan
Menu
Cohort builder

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.

Search term
Author
Year range
Sort
Language
Type
Field
Venue
Topic
Remote Sensing and LiDAR Applications
Retraction
Abstract
Evidence source
Study design
Label agreement
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 ·
Results by year
20002025
Publication date
Categories
Machine labels · sparse coverage
Evidence
Language
Type
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 7 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.

venueno affunlabeled
LiDAR A new tool for forest measurements?
David Evans, Scott D. Roberts, Robert Parker
2006· article· en· The Forestry Chronicle· Environmental Science
distilled prediction:candidate · noneconsensus · none
54
citations
afffundunlabeled
AN ADAPTIVE APPROACH FOR SEGMENTATION OF 3D LASER POINT CLOUD
Zahra Lari, Ayman Habib, Eunju Kwak
2012· 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
54
citations
affunlabeled
Tree detection and diameter estimation based on deep learning
Vincent Grondin, Jean-Michel Fortin, François Pomerleau, Philippe Giguère
2022· article· en· Forestry An International Journal of Forest Research· Environmental Science
distilled prediction:candidate · noneconsensus · none
53
citations
affaboutunlabeled
Automated Highway Sign Extraction Using Lidar Data
Suliman Gargoum, Karim El‐Basyouny, J. Sabbagh, Kenneth L. Froese
2017· article· en· Transportation Research Record Journal of the Transportation Research Board· Environmental Science
distilled prediction:candidate · stsconsensus · none
53
citations
affunlabeled
Learning to reconstruct botanical trees from single images
Bosheng Li, Jacek Kałużny, Jonathan Klein, Dominik L. Michels, Wojtek Pałubicki, Bedřich Beneš +1 more
2021· article· en· ACM Transactions on Graphics· Environmental Science
distilled prediction:candidate · insufficient_payloadconsensus · none
52
citations
affunlabeled
An Overview of Shoreline Mapping by Using Airborne LiDAR
Junbo Wang, Lanying Wang, Shufang Feng, Benrong Peng, Lingfeng Huang, Sarah Narges Fatholahi +2 more
2023· article· en· Remote Sensing· Environmental Science
distilled prediction:candidate · noneconsensus · none
48
citations
afffundvenueunlabeled
The DeLeaves: a UAV device for efficient tree canopy sampling
Guillaume Charron, Thomas Robichaud-Courteau, Hughes La Vigne, Samantha R. Weintraub, A. E. Hill, Douglas Justice +2 more
2020· article· en· Journal of Unmanned Vehicle Systems· Environmental Science
distilled prediction:candidate · noneconsensus · none
48
citations

How this was built: Screen · Findings · About