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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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Traffic Prediction and Management 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.

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.

1,211 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.
1,211 works in the cohort · of 4,299,418page 23 of 25

Labels cover 3 of 1,211 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 1,211 of 1,211 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.

aboutno affunlabeled
Ontario Provincial Enforcement Reporting System
Junna Jiang, Taufiq Hasan, J Hudebine
2015· article· en· TAC 2015: Getting You There Safely - 2015 Conference and Exhibition of the Transportation Association of Canada // ATC: Destination sécurité routière - 2015 Congrès et Exposition de l'Association des transports du Canada· Engineering
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Speed Limit Estimation Using Graph Neural Networks
Fourat Larnaout, Samuel Foucher, Mickaël Germain, Yacine Bouroubi
2025· preprint· en· SSRN Electronic Journal· Engineering
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
A Stochastic Model for Predicting Shockwaves on Freeways
Reza Noroozi, Bruce Hellinga
2015· article· en· Transportation Research Board 94th Annual MeetingTransportation Research Board· Engineering
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
Variable Speed Limits Framework on a Pilot Study on Alberta Highways
A A Guebert, Rey Chow, C Mulyk, I Akhnoukh, Satish Sharma, Scott C. McDonald +1 more
2012· article· en· 2012 CONFERENCE AND EXHIBITION OF THE TRANSPORTATION ASSOCIATION OF CANADA - TRANSPORTATION: INNOVATIONS AND OPPORTUNITIES· Engineering
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Deep Learning Algorithms for Traffic Flow Predictions
Adegoke Ojeniyi, Prince Pal Singh, A Swati Vashisht, Swati Kumari, Karan Karan
2025· article· en· AUIQ technical engineering science.· Engineering
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
Getting simulation "over the hump" as an operational analysis tool
Rob Pringle, Goran Nikolic
2015· article· en· TAC 2015: Getting You There Safely - 2015 Conference and Exhibition of the Transportation Association of Canada // ATC: Destination sécurité routière - 2015 Congrès et Exposition de l'Association des transports du Canada· Engineering
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
Planning of Road Network Monitoring Using GPS and GIS
Martin Trépanier, André Langevin, Fabien Marzolf
2004· article· en· PolyPublie (École Polytechnique de Montréal)· Engineering
machine prediction:candidate · noneconsensus · none
0
citations

How this was built: Screen · Findings · About