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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.

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.

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 5 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 teacher distillation outputs. Candidate is the union; consensus is the intersection.

venueno affno abstractgemma · bibliometricsgpt · bibliometricsmodels split
A Bibliometric Analysis of the Evolution of IoT Applications in Smart Agriculture
Husein Osman Abdullahi, Murni Mahmud, Abdikarim Abi Hassan, Abdifatah Farah Ali
2023· article· fr· Ingénierie des systèmes d information· Agricultural and Biological Sciences
distilled prediction:candidate · bibliometricsconsensus · none
14
citations
afffundno abstractunlabeled
A survey on deep learning applications in wheat phenotyping
Amirhossein Zaji, Zheng Liu, Gaozhi Xiao, Jatinder S. Sangha, Yuefeng Ruan
2022· article· en· Applied Soft Computing· Agricultural and Biological Sciences
distilled prediction:candidate · noneconsensus · none
14
citations
affno abstractunlabeled
Weed Density Estimation Using Semantic Segmentation
Muhammad Asad, Abdul Bais
2020· book-chapter· en· Lecture notes in computer science· Agricultural and Biological Sciences
distilled prediction:candidate · noneconsensus · none
13
citations
affunlabeled
Smart Plant Monitoring System Using IoT Technology
Ankur Kohli, Rohit Kohli, Bhupendra Singh, Jasjit Singh
2019· book-chapter· en· Advances in computational intelligence and robotics book series· Agricultural and Biological Sciences
distilled prediction:candidate · noneconsensus · none
13
citations
affunlabeled
COMPUTER VISION SYSTEM FOR DETECTING ORCHARD TREES FROM UAV IMAGES
Hela Jemaa, W. Bouachir, B. Leblon, N. Bouguila
2022· 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
distilled prediction:candidate · stsconsensus · none
12
citations
affunlabeled
IoT Based Smart Automated Agriculture and Real Time Monitoring System
F. M. Javed Mehedi Shamrat, Alamin Hossain, Tonmoy Roy, Md Ahasanul Adeeb Khan, Ankit Khater, Md Tareq Rahman
2021· article· en· 2021 2nd International Conference on Smart Electronics and Communication (ICOSEC)· Agricultural and Biological Sciences
distilled prediction:candidate · noneconsensus · none
11
citations
venueno affunlabeled
Sorting Raisins by Machine Vision System
Mahdi Abbasgholipour, Mahmoud Omid, Alireza Keyhani, Seydsaeid Mohtasebi
2010· article· en· Modern Applied Science· Agricultural and Biological Sciences
distilled prediction:candidate · noneconsensus · none
10
citations

How this was built: Screen · Findings · About