Estimation of maintenance energy requirements in German shepherd and Labrador retriever dogs in Bangalore, India
Bibliographic record
Abstract
Summary Maintenance energy requirements (MERs) were calculated for 17 German shepherd and 20 Labrador retriever adult dogs using an in‐home prospective dietary trial. The dogs were fed the same dry pet food and body weight, food intake, body condition score and physical activity were monitored for 10 weeks. Labrador retrievers were significantly heavier and had higher body condition scores than German shepherd dogs, but there was no difference between males and females within each group. Body weights remained stable over the study period, with an average daily gain of 9.1 g. Mean (SD) MER was 103.4 (16.3) kcal/kg BW0.75, which was some 20% lower than that currently suggested for moderately active young adult dogs. Individual MER ranged from 66.8 to 141 kcal/kg BW0.75. There were no significant differences in MER between the two breeds, or between males and females within and between the two breeds. There was a significant inverse relationship between MER and body condition score, reflecting the lower energy expenditure of adipose tissue. The lower MER of dogs in this study, relative to previous observations, may reflect climatic and environmental differences and highlight the necessity for accurate estimates of MER in relation to the production and feeding of pet foods.
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 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 | 0.001 |
| 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) | 0.001 | 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".