Sex and strain differences in isotope turnover rates and metabolism in house mice (<i>Mus musculus</i>)
Bibliographic record
Abstract
The rate of nutrient incorporation into most organisms is an unknown but important factor in temporally variable systems. We investigate within-species variation in tissue turnover and metabolic rate among house mice ( Mus musculus L., 1758). By establishing a predictive relationship between tissue turnover rate and metabolic rate, field-based studies could more easily estimate tissue turnover rates using metabolic rate as a surrogate. Here, a diet change was administered using male and female mice of two strains (BALB/c and CBA/J) to test whether a predictive relationship was detectable within a species. Resting metabolic rate (mean values of 1.50–3.64 mL O2·h–1·g–1) and metabolic tissue turnover m (0.02–0.07), were significantly different between sexes, but not between strains. Females of both strains exhibited a nitrogen turnover rate significantly faster than males. Females had less mass than males, which could account for the differences in tissue replacement rates between sexes. The difference in metabolic rate within a species (between strains) may not be large enough to affect the rate of tissue turnover, suggesting that field researchers may be able to assume similar turnover rates among same-sex individuals of the same species. However, it may be important to account for sexual dimorphism when studying tissue turnover and metabolism.
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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 | 0.001 | 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 | 0.000 | 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".