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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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Sports Analytics and Performance
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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.

1,185 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.
1,185 works in the cohort · of 4,299,418page 3 of 24

Labels cover 3 of 1,185 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 1,185 of 1,185 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.

affunlabeled
Going All in on AI
Michael L. Naraine, Liz Wanless
2020· article· en· Sports Innovation Journal· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
30
citations
affunlabeled
Effects of expertise on football betting
Yasser Khazaal, Anne Chatton, Joël Billieux, Lucio Bizzini, Grégoire Monney, Emmanuelle Frésard +5 more
2012· article· en· Substance Abuse Treatment Prevention and Policy· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
30
citations
affunlabeled
Major League Baseball Managers: Do They Matter?
Dennis L. Smart, Jason A. Winfree, Richard Wolfe
2008· article· en· Journal of Sport Management· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
29
citations
affno abstractunlabeled
Testing Game Theory in the Field: Swedish LUPI Lottery Games
Robert Östling, Joseph Tao‐yi Wang, Eileen Y. Chou, Colin F. Camerer
2010· article· en· SSRN Electronic Journal· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
28
citations
affno abstractgemma · no categorygpt · no categorymodels agree
An Event-Based Pool Physics Simulator
Will Leckie, Michael Greenspan
2006· book-chapter· en· Lecture notes in computer science· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
26
citations
affunlabeled
Analysis of substitution times in soccer
Rajitha M. Silva, Tim B. Swartz
2016· article· en· Journal of Quantitative Analysis in Sports· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
25
citations
affunlabeled
Bettor Belief in the “Hot Hand”
Rodney J. Paul, Andrew P. Weinbach, Brad R. Humphreys
2012· article· en· Journal of Sports Economics· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
25
citations
affunlabeled
Understanding draws in Elo rating algorithm
Leszek Szczeciński, Aymen Djebbi
2020· article· en· Journal of Quantitative Analysis in Sports· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
23
citations
affunlabeled
New Insights Involving the Home Team Advantage
Tim B. Swartz, Adriano Arce
2014· article· en· International Journal of Sports Science & Coaching· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
22
citations
aboutno affunlabeled
The National Hockey League and Cross-Border Fandom
Brian M. Mills, Mark S. Rosentraub
2014· article· en· Journal of Sports Economics· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
21
citations
affunlabeled
Predicting plays in the National Football League
Craig Fernandes, Ronen Yakubov, Yuze Li, Amrit Kumar Prasad, Timothy C. Y. Chan
2019· article· en· Journal of Sports Analytics· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
21
citations
affunlabeled
Rematches in Boxing and Other Sporting Events
J. Atsu Amegashie, Edward Kutsoati
2005· article· en· Journal of Sports Economics· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
20
citations
affunlabeled
Cracking the Black Box
Xiangyu Sun, Jack Davis, Oliver Schulte, Guiliang Liu
2020· preprint· en· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
20
citations
afffundunlabeled
Level-0 Models for Predicting Human Behavior in Games
James R. Wright, Kevin Leyton‐Brown
2019· article· en· Journal of Artificial Intelligence Research· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
19
citations
affunlabeled
Semi-Automated Gameplay Analysis by Machine Learning
Finnegan Southey, Gang Xiao, Robert C. Holte, Mark Trommelen, John W. Buchanan
2005· article· en· Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
19
citations
affno abstractunlabeled
A study of the powerplay in one-day cricket
Rajitha M. Silva, Ananda B. W. Manage, Tim B. Swartz
2015· article· en· European Journal of Operational Research· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
19
citations

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