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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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Image Retrieval and Classification Techniques
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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.

1,015 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.
1,015 works in the cohort · of 4,299,418page 6 of 21

Labels cover 2 of 1,015 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 1,015 of 1,015 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.

venueno affunlabeled
A New Approach for the Simplification of Contours
Türkay Gökgöz, Mehmet Timur Selçuk
2004· article· en· Cartographica The International Journal for Geographic Information and Geovisualization· Computer Science
machine prediction:candidate · noneconsensus · none
13
citations
affunlabeled
Effective image and video mining
Rokia Missaoui, Roman M. Palenichka
2005· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
13
citations
affunlabeled
Artificial intelligence and imagery
Janice Glasgow
2002· article· en· [1990] Proceedings of the 2nd International IEEE Conference on Tools for Artificial Intelligence· Computer Science
machine prediction:candidate · noneconsensus · none
12
citations
affunlabeled
Multi-Magnification Image Search in Digital Pathology
Maral Rasoolijaberi, Morteza Babaei, Abtin Riasatian, Sobhan Hemati, Parsa Ashrafi, Ricardo González +1 more
2022· article· en· IEEE Journal of Biomedical and Health Informatics· Computer Science
machine prediction:candidate · noneconsensus · none
12
citations
affunlabeled
Content-Based Image Retrieval
Ming Zhang, Reda Alhajj
2009· book-chapter· en· IGI Global eBooks· Computer Science
machine prediction:candidate · noneconsensus · none
12
citations
affno abstractunlabeled
Toward Cross-Language and Cross-Media Image Retrieval
Ahmed Id Oumohmed, Max Mignotte, Jian‐Yun Nie
2005· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
11
citations
venueno affunlabeled
Finding Images in an Online Public Access Catalogue: Analysis of User Queries, Subject Headings, and Description Notes / Le repérage d'images dans un catalogue en ligne à accès libre : analyse des requêtes des utilisateurs, des vedettes-matière, et des notes descriptives
Youngok Choi, Ingrid Hsieh‐Yee
2010· article· fr· Canadian Journal of Information and Library Science· Computer Science
machine prediction:candidate · scholarly_communicationconsensus · none
11
citations

How this was built: Screen · Findings · About