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
Methane Hydrates and Related Phenomena
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.

3,528 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.
3,528 works in the cohort · of 4,299,418page 51 of 71

Labels cover 3 of 3,528 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 3,528 of 3,528 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
Data for EMSL Project 51615 from June 2021
Irina Novikova, Walid A. Houry, Jeffrey Lynham, Marim Barghash, Mark Mabanglo, Thiago Vargas Seraphim +1 more
2021· dataset· en· OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information)· Environmental Science
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Data for EMSL Project 50463 from November 2019
Adrian Tsang, Ronald P. de Vries, Miia Mäkelä, Mikael Andersen
2019· dataset· en· OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information)· Environmental Science
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Data for EMSL Project 51615 from November 2020
Irina Novikova, Walid A. Houry, Jeffrey Lynham, Marim Barghash, Mark Mabanglo, Thiago Vargas Seraphim +1 more
2020· dataset· en· OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information)· Environmental Science
machine prediction:candidate · noneconsensus · none
0
citations
venueno affno abstractunlabeled
10.1016/s0967-0653(98)80443-2
2000· article· en· Time to knit· Environmental Science
machine prediction:candidate · insufficient_payloadconsensus · insufficient_payload
0
citations
affno abstractunlabeled
Data for EMSL Project 51615 from November 2020
Irina Novikova, Walid A. Houry, Jeffrey Lynham, Marim Barghash, Mark Mabanglo, Thiago Vargas Seraphim +1 more
2020· dataset· en· OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information)· Environmental Science
machine prediction:candidate · noneconsensus · none
0
citations
venueno affno abstractunlabeled
10.1016/0967-0653(95)90089-h
2000· article· en· Time to knit· Environmental Science
machine prediction:candidate · insufficient_payloadconsensus · insufficient_payload
0
citations
affno abstractunlabeled
Data for EMSL Project 51615 from September 2022
Irina Novikova, Walid A. Houry, Jeffrey Lynham, Marim Barghash, Mark Mabanglo, Thiago Vargas Seraphim +1 more
2022· dataset· en· OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information)· Environmental Science
machine prediction:candidate · noneconsensus · none
0
citations
venueno affno abstractunlabeled
10.1016/0967-0653(96)82368-4
2000· article· en· Time to knit· Environmental Science
machine prediction:candidate · insufficient_payloadconsensus · insufficient_payload
0
citations
aboutno affno abstractunlabeled
Does the Labrador Water reach the CANIGO area
Roa-Lobo José, V. Marimar, Carlos Andrés, G. Juana, Patricio Javier, M. Leire +2 more
2001· article· en· AGUFM· Environmental Science
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Data for EMSL Project 51072 from January 2021
Adrian Tsang, Ronald P. de Vries, Miia Mäkelä
2021· dataset· en· OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information)· Environmental Science
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Data for EMSL Project 51366 from January 2022
Steven Hallam, Hugh Mitchell, Ryan Ziels, Daniel Mulat, Brandon Kieft, Elizabeth McDaniel
2022· dataset· en· OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information)· Environmental Science
machine prediction:candidate · noneconsensus · none
0
citations
venueno affno abstractunlabeled
10.1016/s0967-0653(97)80868-x
2000· article· en· Time to knit· Environmental Science
machine prediction:candidate · insufficient_payloadconsensus · insufficient_payload
0
citations
affno abstractunlabeled
Data for EMSL Project 51065 from July 2020
Laura Hug
2020· dataset· en· OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information)· Environmental Science
machine prediction:candidate · noneconsensus · none
0
citations
venueno affno abstractunlabeled
10.1016/s0967-0653(98)80216-0
2000· article· en· Time to knit· Environmental Science
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations
affno abstractunlabeled
Data for EMSL Project 51615 from November 2020
Irina Novikova, Walid A. Houry, Jeffrey Lynham, Marim Barghash, Mark Mabanglo, Thiago Vargas Seraphim +1 more
2020· dataset· en· OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information)· Environmental Science
machine prediction:candidate · noneconsensus · none
0
citations
venueno affno abstractunlabeled
10.1016/s0967-0653(97)83324-8
2000· article· en· Time to knit· Environmental Science
machine prediction:candidate · insufficient_payloadconsensus · insufficient_payload
0
citations
affno abstractunlabeled
Methane Oxidation
Lisa Y. Stein
2019· book-chapter· en· Encyclopedia of Astrobiology· Environmental Science
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Data for EMSL Project 50379 from February 2020
Adrian Tsang, Ronald P. de Vries, Miia Mäkelä, María Victoria Aguilar Pontes
2020· dataset· en· OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information)· Environmental Science
machine prediction:candidate · noneconsensus · none
0
citations
venueno affno abstractunlabeled
10.1016/0967-0653(93)91156-7
2000· article· en· Time to knit· Environmental Science
machine prediction:candidate · insufficient_payloadconsensus · insufficient_payload
0
citations
venueno affno abstractunlabeled
10.1016/0967-0653(94)91693-4
2000· article· en· Time to knit· Environmental Science
machine prediction:candidate · insufficient_payloadconsensus · insufficient_payload
0
citations
affno abstractunlabeled
Data for EMSL Project 51366 from January 2022
Steven Hallam, Hugh Mitchell, Ryan Ziels, Daniel Mulat, Brandon Kieft, Elizabeth McDaniel
2022· dataset· en· OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information)· Environmental Science
machine prediction:candidate · noneconsensus · none
0
citations
venueno affno abstractunlabeled
10.1016/s0967-0653(97)87251-1
2000· article· en· Time to knit· Environmental Science
machine prediction:candidate · insufficient_payloadconsensus · insufficient_payload
0
citations
affaboutunlabeled
Laurentian lakes dataset
Roxane Maranger, Morgan Botrel, Nicolas Fortin St‐Gelais
2019· dataset· en· Zenodo (CERN European Organization for Nuclear Research)· Environmental Science
machine prediction:candidate · noneconsensus · none
0
citations
venueno affno abstractunlabeled
10.1016/0967-0653(96)82445-8
2000· article· en· Time to knit· Environmental Science
machine prediction:candidate · insufficient_payloadconsensus · insufficient_payload
0
citations
affno abstractunlabeled
Data for EMSL Project 51065 from July 2020
Laura Hug
2020· dataset· en· OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information)· Environmental Science
machine prediction:candidate · noneconsensus · none
0
citations
venueno affno abstractunlabeled
10.1016/0967-0653(95)99281-u
2000· article· en· Time to knit· Environmental Science
machine prediction:candidate · insufficient_payloadconsensus · insufficient_payload
0
citations

How this was built: Screen · Findings · About