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Record W2031995346 · doi:10.1080/13518470500531051

An application of expert information to win betting on the Kentucky Derby, 1981–2005

2006· article· en· W2031995346 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueEuropean Journal of Finance · 2006
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicSports Analytics and Performance
Canadian institutionsUniversity of British ColumbiaAgricultural Institute of Canada
FundersUniversity of Pennsylvania
KeywordsInefficiencyHorse racingMileRace (biology)Index (typography)Test (biology)Expert opinionDatabase transactionActuarial scienceDemographic economicsEconomicsComputer scienceGeographyDatabaseMedicineMicroeconomicsWorld Wide Web

Abstract

fetched live from OpenAlex

Abstract The Kentucky Derby features top three-year-old thoroughbred horses. Run at miles, it is typically at least 1/8 mile longer than any of the horses has raced before. This extra distance, usually combined with a large field, makes the race a difficult test of stamina for horses this young. Bettors, because there is no direct evidence of whether a horse has the stamina to compete effectively at miles, are also challenged. The informational content of one publicly available, pedigree-based measure of stamina, the Dosage Index, is used with simple performance measures to identify a semi-strong-form inefficiency, and to create a betting scheme based on the optimal capital growth model that merges these criteria with the public’s opinion. Statistically significant profits, net of transaction costs, could have been achieved during the period 1981 to 2005.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.663
Threshold uncertainty score0.291

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.014
GPT teacher head0.201
Teacher spread0.187 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it