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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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Smart Agriculture and AI
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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.

930 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.
930 works in the cohort · of 4,299,418page 11 of 19

Labels cover 1 of 930 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 930 of 930 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
Rice Leaf Disease Diagnosis Using Dense EfficientNet Model
E. M. Roopa Devi, R. Shanthakumari, R. Rajadevi, Anusuyaa, Harini, Lokesh Lokesh
2024· book-chapter· en· Lecture notes in networks and systems· Agricultural and Biological Sciences
machine prediction:candidate · noneconsensus · none
2
citations
fundno affno abstractunlabeled
Advanced Computing
Deepak Garg, V. A. Narayana, Ponnuthurai Nagaratnam Suganthan, Jaume Anguera, Vijaya Kumar Koppula, Suneet Kumar Gupta
2023· book· en· Communications in computer and information science· Agricultural and Biological Sciences
machine prediction:candidate · noneconsensus · none
2
citations
aboutno affunlabeled
A Comparison of Sentinel-1 Biased and Unbiased Coherence for Crop Monitoring and Classification
Zhao Qinxin, Qinghua Xie, Xing Peng, Yusong Bao, Tonglu Jia, Linwei Yue +2 more
2024· 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· Agricultural and Biological Sciences
machine prediction:candidate · noneconsensus · none
2
citations
affunlabeled
AI Driven Detecting plants and Animal Diseases
S. S. Barakkath Shabana, E. Sharmesh, S. Jagadeeswari
2025· article· en· Agricultural and Biological Sciences
machine prediction:candidate · noneconsensus · none
2
citations
affno abstractunlabeled
Two-view fine-grained classification of plant species
Voncarlos M. Araujo, Alceu S. Britto, Luiz S. Oliveira, Alessandro L. Koerich
2021· preprint· en· Neurocomputing· Agricultural and Biological Sciences
machine prediction:candidate · noneconsensus · none
2
citations
affunlabeled
Survey Paper: Plant Disease Detection using CNN
Sakshi Rudrawar, Yogini Pujari, Apeksha Savant, Prof. Mrutyunjay Kumar
2023· article· en· International Journal of Advanced Research in Science Communication and Technology· Agricultural and Biological Sciences
machine prediction:candidate · noneconsensus · none
1
citations
affno abstractunlabeled
Introduction to AI in Agriculture
Shivalika Sood, Nitin Goyal, Amanjot Singh Syan
2025· book-chapter· en· Agricultural and Biological Sciences
machine prediction:candidate · noneconsensus · none
1
citations

How this was built: Screen · Findings · About