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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.

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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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Remote Sensing in Agriculture
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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.

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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.

1,867 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.
1,867 works in the cohort · of 4,299,418page 31 of 38

Labels cover 0 of 1,867 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 1,867 of 1,867 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.

affaboutunlabeled
ESTIMATING CANOLA’S BIOPHYSICAL PARAMETERS FROM TEMPORAL, SPECTRAL, AND POLARIMETRIC IMAGERY USING MACHINE LEARNING APPROACHES
Omid Reisi Gahrouei, Saeid Homayouni, Abdolreza Safari
2019· 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
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
МОДЕЛИРОВАНИЕ И КАРТИРОВАНИЕ ВЛИЯНИЯ ИЗМЕНЕНИЙ КЛИМАТА НА МНОГОЛЕТНЮЮ МЕРЗЛОТУ В РЕГИОНЕ СО СЛОЖНЫМ РЕЛЬЕФОМ С ВЫСОКИМ ПРОСТРАНСТВЕННЫМ РАЗРЕШЕНИЕМ
Ю. ЧЖАН, С. ВАН, Р. ФРЕЙЗЕР, И. ОЛТХОФ, В. ЧЭНЬ, Д. МАКЛЕННАН +2 more
2025· article· ru· Геоинфо· Environmental Science
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
Comment on essd-2021-34
Jichong Han, Zhao Zhang, Yuchuan Luo, Juan Cao, Liangliang Zhang, Jing Zhang +1 more
2021· peer-review· en· Environmental Science
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations
venueno affunlabeled
Spectral Behavior of Pinus elliottii, Subjected to Drought in Southern Brazil
Géssyca Fernanda de Sena Oliveira, Uilian do Nascimento Barbosa, José Jorge Monteiro, Diogo José Oliveira Pimentel, Julianne Moura da Silva, Lorena Melo +8 more
2019· article· en· Journal of Agricultural Science· Environmental Science
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
Towards Monitoring Biodiversity from Space
2024· dissertation· en· Zurich Open Repository and Archive (University of Zurich)· Environmental Science
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Johnson_House41_JUSTHR_041ev.pdf
2018· dataset· zh· Syracuse University Qualitative Data Repository· Environmental Science
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations
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How this was built: Screen · Findings · About