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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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Multimodal Machine Learning Applications
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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.

476 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.
476 works in the cohort · of 4,299,418page 5 of 10

Labels cover 3 of 476 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 476 of 476 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
StableYolo: Optimizing Image Generation for Large Language Models
Harel Berger, Aidan Dakhama, Zishuo Ding, Karine Even-Mendoza, David Kelly, Héctor D. Menéndez +2 more
2023· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
4
citations
affunlabeled
DOM-Q-NET: Grounded RL on Structured Language
Sheng Jia, Jamie Kiros, Jimmy Ba
2019· article· en· International Conference on Learning Representations· Computer Science
machine prediction:candidate · noneconsensus · none
3
citations
fundno affunlabeled
Neural ranking models for document retrieval
Mohamed Trabelsi, Zhiyu Chen, Brian D. Davison, Jeff Heflin
2021· preprint· en· Information Retrieval· Computer Science
machine prediction:candidate · noneconsensus · none
3
citations
affunlabeled
Areas of Attention for Image Captioning
Marco Pedersoli, Thomas W. Lucas, Cordelia Schmid, Jakob Verbeek
2016· preprint· en· arXiv (Cornell University)· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
affvenueunlabeled
Bias Dilemma
Oisín Deery, Katherine Bailey
2022· article· en· Feminist Philosophy Quarterly· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
afffundunlabeled
One-Shot Informed Robotic Visual Search in the Wild
Karim Koreitem, Florian Shkurti, Travis Manderson, Wei-Di Chang, Juan Camilo Gamboa Higuera, Gregory Dudek
2020· preprint· en· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
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