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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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Atmospheric and Environmental Gas Dynamics
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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
fundfunder
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.

5,307 results · 1 filter active ·
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20002025
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Machine labels · sparse coverage
Evidence
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An unlabeled work is unknown, not a negative. Label coverage is reported on every query.
5,307 works in the cohort · of 4,299,418page 91 of 107

Labels cover 5 of 5,307 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 5,307 of 5,307 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.

fundno affunlabeled
Reply on RC1
Josh Laughner
2023· peer-review· en· Environmental Science
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Comment on egusphere-2023-1127
2023· peer-review· en· Environmental Science
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
Comment: : Not a gale, but a breeze
Siobhan Lismore-Scott
2017· article· en· Industrial Minerals· Environmental Science
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Comment on egusphere-2023-65
István Dunkl, Nicole S. Lovenduski, Alessio Collalti, Vivek K. Arora, Tatiana Ilyina, Victor Brovkin
2023· peer-review· en· Environmental Science
machine prediction:candidate · noneconsensus · none
0
citations
affaboutunlabeled
Comment on acp-2021-549
2021· peer-review· en· Environmental Science
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Radiative effect of aerosols on the Arctic Climate during Spring
Rong‐Ming Hu
2003· article· en· Seventh Conference on Polar Meteorology and Oceanography and Joint Symposium on High-Latitude Climate Variations· Environmental Science
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
blc20150924_03.txt
Robyn Fiori
2020· dataset· en· Harvard Dataverse· Environmental Science
machine prediction:candidate · insufficient_payloadconsensus · insufficient_payload
0
citations
affunlabeled
The calibration of the MOPITT instrument
Jiansheng Zou, F. Nichitiu, J. R. Drummond
2003· article· en· Environmental Science
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
Overcoming the oil price decline
E. D. Hughes
2015· article· en· Industrial Minerals· Environmental Science
machine prediction:candidate · noneconsensus · none
0
citations
afffundunlabeled
Comment on egusphere-2023-137
Alex Mavrovic, Oliver Sonnentag, Juha Lemmetyinen, Jennifer L. Baltzer, Christophe Kinnard, Alexandre Roy
2023· peer-review· en· Environmental Science
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations
affno abstractunlabeled
Arctic methane as an amplifier of global warming
Torben R. Christensen, Vivek K. Arora, Michael Gauss, Lena Höglund-Isaksson, Frans‐Jan W. Parmentier
2018· article· en· AGU Fall Meeting Abstracts· Environmental Science
machine prediction:candidate · noneconsensus · none
0
citations

How this was built: Screen · Findings · About