The Effects of Varying Concentrations of Dietary Protein and Fat on Blood Gas, Hematologic Serum Chemistry, and Body Temperature Before and After Exercise in Labrador Retrievers
Bibliographic record
Abstract
Optimal dietary protocols for the athletic canine are often defined by requirements for endurance athletes that do not always translate into optimal dietary interventions for all canine athletes. Prior research studying detection dogs suggests that dietary fat sources can influence olfaction; however, as fat is added to the diet the protein calories can be diminished potentially resulting in decreased red blood cell counts or albumin status. Optimal macronutrient profile for detection dogs may be different considering the unique work they engage in. To study a calorically low protein: high fat (18:57% ME), high protein: high fat (27:57% ME), and high protein: low fat (27:32% ME) approach to feeding, 17 dogs were provided various diets in a 3 × 3 cross over design. Dogs were exercised on a treadmill and blood was taken pre-exercise, immediately post-exercise, 10- and 20-min post-exercise to assess complete blood count, serum chemistry, blood gases, and cortisol; as well as rectal and core body temperature. Exercise induced a decrease in serum phosphorus, potassium, and increases in non-esterified fatty acids and cortisol typical of moderate exercise bouts. A complete and balanced high protein: high-fat diet (27:57% ME) induced decreases in serum cortisol and alkaline phosphatase. Corn oil top dressed low protein: high-fat diet (18:57% ME) induced a slightly better thermal recovery than a complete and balanced high protein: high fat diet and a high protein: low fat (27%:32% ME) diet suggesting some mild advantages when using the low protein: high fat diet that warrant further investigation regarding optimal protein and fat calories and thermal recovery.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".