PSI-B-41 Late-Breaking: Effect of age, sex, reproductive status, body composition, and environmental temperature on the basal metabolic rate of working Labrador Retrievers
Bibliographic record
Abstract
Abstract As the pet industry continues to grow, understanding the needs of different demographics of canines is becoming increasingly important to ensure optimal nutrition. Energy requirements have been shown to vary based on individual factors, but few trials using many dogs under the same controls exist. The objective of this study was to investigate the effect of age, sex, reproductive status, body composition, and environmental temperature on the BMR of Labrador Retrievers (Labs). An open-circuit indirect calorimetry machine attached to a chamber was used to determine resting BMR in 96 Labs. Body composition in 33 Labs of varying age was determined using DXA scans to determine effect of lean/fat mass on BMR. Cooling and heating implements were applied to the chamber to determine temperature effect on BMR. Each of the following demographics were compared using a mixed model: male, female, intact, altered, young (6mo-2yo), adult (3-6yo), and senior (7yo+). Mean BMR for all dogs was 130 (27) kcal/kg0.75. Males were significantly higher at 136 (28) kcal/kg0.75 than females at 125 (25) kcal/kg0.75 (P = 0.045). Intact Labs were significantly higher at 121 (3) kcal/kg0.75 compared to altered Labs at 109 (25) kcal/kg0.75 (P < 0.001). Young and adult Labs had BMRs of 136 (19) kcal/kg0.75 and 135 (29) kcal/kg0.75, respectively, which was significantly higher than seniors at 120 (26) kcal/kg0.75. Body composition comparisons showed significant negative linear relationships between BMR and fat mass (P < 0.001) and positive linear relationships between BMR and lean mass (P < 0.001). BMR was found to have a negative linear relationship (R2=0.51) from cool to warm temperatures but was elevated at both cold (< 5°C) and hot (>35°C) temperatures (P < 0.001). The determination of energy requirements found in this study prove useful as the pet industry develops diets specific to the needs of different demographics of canines.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".