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Maintenance of bodyweight during a multiple‐day chuckwagon race meet

2002· article· en· W2033651194 on OpenAlexaff
L.K. Warren, A. WHELEN

Bibliographic record

VenueEquine Veterinary Journal · 2002
Typearticle
Languageen
FieldVeterinary
TopicVeterinary Equine Medical Research
Canadian institutionsAgriculture Food and Rural Development
Fundersnot available
KeywordsRace (biology)MedicineHorseBody weightAnimal scienceWeight lossBiologyObesityInternal medicine

Abstract

fetched live from OpenAlex

The gruelling race schedules maintained by horses competing in chuckwagon racing raises concern for the horses' ability to recover quickly and continue to perform at a high level. The amount of bodyweight lost and the time required for recovery of this weight loss have been used to assess the level of stress imposed on horses competing in various multiple-day events. In this study, bodyweights were obtained from 40 Thoroughbred geldings (mean +/- s.e.; bodyweight 521.5 +/- 4.4 kg) before and after racing during a 5 day chuckwagon race meet. Body condition score (BCS) was determined on the first and last day of competition. Comparisons were based on the number of consecutive days the horse raced. Average bodyweight loss (P = 0.039) from each race was 3.5 +/- 0.3 kg (0.7% of initial bodyweight) and was not affected by the number of days the horse raced. The largest bodyweight deficit (P = 0.005) occurred within the 24 h period after their first race (5.3 +/- 0.5 kg; 1.0% of initial bodyweight). Horses racing on 2-5 consecutive days retained a 4.8 +/- 0.3 kg deficit (P = 0.01), which was maintained throughout the remainder of the race meet. Horses began and ended the race meet with a BCS of 4.9 +/- 0.2 and 4.7 +/- 0.2, respectively (using the 1 to 9 BCS system). Although chuckwagon horses compete in a strenuous event on several consecutive days, they appear to be managed well and have the ability to maintain their bodyweight despite the physical and psychological demands of frequent racing.

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.

How this classification was reachedexpand

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.928
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0100.000

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 head0.106
GPT teacher head0.349
Teacher spread0.243 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2002
Admission routes1
Has abstractyes

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