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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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Meteorological Phenomena and Simulations
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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
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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.

1,904 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.
1,904 works in the cohort · of 4,299,418page 19 of 39

Labels cover 1 of 1,904 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 1,904 of 1,904 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.

affunlabeled
DCMIP2016: The Splitting Supercell Test Case
Colin M. Zarzycki, Christiane Jablonowski, James Kent, P. H. Lauritzen, Ramachandran D. Nair, Kevin A. Reed +21 more
2018· preprint· en· Earth and Planetary Sciences
machine prediction:candidate · noneconsensus · none
4
citations
affaboutunlabeled
Wind Flow Characteristics of a Model Downburst
Junayed Chowdhury, Jubayer Chowdhury, Dan Parvu, Mohammad Amin Karami, Horia Hangan
2018· article· en· Earth and Planetary Sciences
machine prediction:candidate · noneconsensus · none
4
citations
affunlabeled
Generating Physically-Consistent Satellite Imagery for Climate Visualizations
Björn Lütjens, Brandon Leshchinskiy, Océane Boulais, Farrukh Chishtie, Natalia Díaz-Rodríguez, Margaux Masson-Forsythe +8 more
2024· article· en· IEEE Transactions on Geoscience and Remote Sensing· Earth and Planetary Sciences
machine prediction:candidate · noneconsensus · none
4
citations
afffundno abstractunlabeled
Data Assimilation for Large-Scale Computational Models
Mohammad Khalil, Abhijit Sarkar
2013· article· en· 54th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference· Earth and Planetary Sciences
machine prediction:candidate · noneconsensus · none
4
citations
fundno affunlabeled
Improving the Laplace transform integration method
Peter Lynch, Colm Clancy
2015· article· en· Quarterly Journal of the Royal Meteorological Society· Earth and Planetary Sciences
machine prediction:candidate · noneconsensus · none
3
citations
affunlabeled
Modeling of High Intensity Winds
Horia Hangan, Pooyan Hashemi-Tari, Jong-Dae Kim
2008· article· en· Structures Congress 2008· Earth and Planetary Sciences
machine prediction:candidate · noneconsensus · none
3
citations
affunlabeled
Multi-model ensemble for long-term statistical trend analysis of observed gridded precipitation and temperature data in the São Francisco River Basin, Brazil
Gabriel Jaime Maya Vasco, Rodrigo de Queiroga Miranda, Jussara Freire de Souza Viana, Danielle de Almeida Bressiani, Eduardo Mário Mendiondo, Richarde Marques da Silva +3 more
2024· article· en· Revista Brasileira de Recursos Hídricos· Earth and Planetary Sciences
machine prediction:candidate · noneconsensus · none
3
citations
affunlabeled
A quest for precipitation attractors in weather radar archives
Loris Foresti, Bernat Puigdomènech Treserras, Daniele Nerini, Aitor Atencia, Marco Gabella, Ioannis V. Sideris +2 more
2024· article· en· Nonlinear processes in geophysics· Earth and Planetary Sciences
machine prediction:candidate · noneconsensus · none
3
citations

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