Effect of increased dietary protein and decreased dietary carbohydrate on performance and body composition in racing Greyhounds
Bibliographic record
Abstract
OBJECTIVE: To determine effects of increased dietary protein and decreased dietary carbohydrate on hematologic variables, body composition, and racing performance in Greyhounds. ANIMALS: 8 adult Greyhounds. PROCEDURE: Dogs were fed a high-protein (HP; 37% metabolizable-energy [ME] protein, 33% ME fat, 30% ME carbohydrate) or moderate-protein (MP; 24% ME protein, 33% ME fat, 43% ME carbohydrate) extruded diet for 11 weeks. Dogs subsequently were fed the other diet for 11 weeks (crossover design). Dogs raced a distance of 500 m twice weekly. Rectal temperature, hematologic variables before and after racing, plasma volume, total body water, body weight, average weekly food intake, and race times were measured at the end of each diet period. RESULTS: When dogs were fed the MP diet, compared with the HP diet, values (mean +/- SD) differed significantly for race time (32.43 +/- 0.48 vs 32.61 +/- 0.50 seconds), body weight (32.8 +/- 2.5 vs 32.2 +/- 2.9 kg), Hct before (56 +/- 4 vs 54 +/- 6%) and after (67 +/- 3 vs 64 +/- 8%) racing, and glucose (131 +/- 16 vs 151 +/- 27 mg/dl) and triglyceride (128 +/- 17 vs 104 +/- 28 mg/dl) concentrations after racing. CONCLUSIONS AND CLINICAL RELEVANCE: Greyhounds were 0.18 seconds slower (equivalent to 0.08 m/s or 2.6 m) over a distance of 500 m when fed a diet with increased protein and decreased carbohydrate. Improved performance attributed to feeding meat to racing Greyhounds apparently is not attributable to increased dietary protein and decreased dietary carbohydrate.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".