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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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Mercury impact and mitigation studies
Retraction
Abstract
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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.

3,785 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.
3,785 works in the cohort · of 4,299,418page 49 of 76

Labels cover 3 of 3,785 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 3,785 of 3,785 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.

afffundno abstractunlabeled
Green synthesis of novel titanomagnetite nanoparticles for oil spill cleanup
Afif Hethnawi, Osama Kashif, Robin Jeong, Farad Sagala, Kotaybah Hashlamoun, Abdallah D. Manasrah +1 more
2023· article· en· Colloids and Surfaces A Physicochemical and Engineering Aspects· Environmental Science
machine prediction:candidate · noneconsensus · none
9
citations
afffundno abstractunlabeled
Methylmercury effects on avian brains
Claire Bottini, Scott A. MacDougall‐Shackleton
2023· review· en· NeuroToxicology· Environmental Science
machine prediction:candidate · noneconsensus · none
9
citations
affaboutunlabeled
A Fluvial Mercury Budget for Lake Ontario
Joseph S. Denkenberger, Charles T. Driscoll, Edward F. Mason, Brian A. Branfireun, Ashley Warnock
2014· article· en· Environmental Science & Technology· Environmental Science
machine prediction:candidate · noneconsensus · none
9
citations
afffundunlabeled
Behavior of mercury in snow from different latitudes
Marc Amyot, Janick D. Lalonde, Parisa A. Ariya, Ashu Dastoor
2003· article· en· Journal de Physique IV (Proceedings)· Environmental Science
machine prediction:candidate · noneconsensus · none
8
citations
afffundno abstractunlabeled
Selenate bioreduction in a large in situ field trial
M. Jim Hendry, Lisa Kirk, Jeff Warner, Shannon Shaw, Brent Peyton, Erin E. Schmeling +1 more
2024· article· en· The Science of The Total Environment· Environmental Science
machine prediction:candidate · noneconsensus · none
8
citations
fundno affno abstractunlabeled
Selenite and Selenate Effects on Mercury (Hg2+) Uptake and Distribution in Tilapia, Oreochromis niloticus L., Assessed by Chronic Bioassay
Gabriel Gustinelli Arantes de Carvalho, Jakeline Galvão de França, Danielle de Carla Dias, Júlio Vicente Lombardi, M. J. R. de Paiva, Sebastião Marcos Ribeiro de Carvalho +2 more
2008· article· en· Bulletin of Environmental Contamination and Toxicology· Environmental Science
machine prediction:candidate · noneconsensus · none
8
citations

How this was built: Screen · Findings · About