Total Carbon Dioxide (TCO2) Concentrations in Thoroughbred and Quarter Racehorses in Louisiana
Bibliographic record
Abstract
The TCO2 (total carbon dioxide) test is performed on the blood of racehorses as a means of combatting the practice of administering alkalizing agents. This study evaluated serum TCO2 concentrations and factors influencing concentration of TCO2 in Thoroughbred and Quarter Horses. The normality of data were evaluated with a Shapiro-Wilk test. Mann-Whitney tests and Kruskal-Wallis tests were used against different effects. When a fixed effect was detected, Dunn's post-hoc comparisons were performed. The median pre-race serum TCO2 concentration (32.20 mmol/L (interquartile range (IQR): 30.80–33.50)) was higher than that of post-race samples (26.70 mmol/L (IQR: 24.55–29.25)) (P < .0001). The median TCO2 concentrations in pre-race samples were different between Thoroughbred (32.40 mmol/L (IQR: 30.90–33.60)) and Quarter Horses (31.30 mmol/L (IQR: 30.00–32.50)) (P < .0001). The median pre-race TCO2 concentrations were 32.75 (IQR: 31.40–33.90), 31.40 (IQR: 29.80–32.80), 32.50 (IQR: 31.20–33.88), and 31.60 (IQR 30.00–32.70) mmol/L in racehorses at Fair Grounds, Louisiana Downs, Delta Downs, and Evangeline Downs racetracks, respectively (P < .0001). The total serum TCO2 concentrations in Thoroughbred and Quarter Horse racehorses were affected by seasonal temperature variation (P < .0001). A smaller sample size was available for post-race samples (n = 205) and Quarter Horse pre-race samples (n = 351). The results of this study indicated that the breed, seasonal temperature variation, pre-race or post-race sampling, and track location are strongly correlated to total TCO2 concentrations. It was not clear whether the statistically significant differences in TCO2 levels among racetracks in Louisiana were due to location of racetracks and/or seasonal temperature variation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".