The role of dietary fiber content on energy metabolism, thermogenesis, and leptin in Chevrier’s field mouse (<i>Apodemus</i> <i>chevrieri</i>)
Bibliographic record
Abstract
Food quality and availability are important factors influencing the survival and reproduction of animals. The aim of the present study was to examine the effect of dietary fiber content high-fiber (HF) diet treatment or low-fiber (LF) diet treatment) on energy metabolism, thermogenesis, and leptin concentrations in Chevrier’s field mouse (Apodemus chevrieri (Milne-Edwards, 1868)) (Mammalia: Rodentia: Muridae). Mice on the HF treatment showed a lower body mass compared with LF treatment from day 27 to day 37, and a lower but insignificant body mass to day 71. Dry matter intake (DMI) and gross energy intake (GEI) were greater in HF compared with LF, whereas the digestible energy intake (DEI) was similar for both treatments. Nonshivering thermogenesis (NST) decreased in HF mice, whereas LF mice remained stable; no significant differences were detected in the basal metabolic rate (BMR), uncoupling protein 1 (UCP1) content in brown adipose tissue (BAT), or the levels of serum tri-iodothyronine (T3) and thyroxine (T4) between HF and LF mice. Although there were no differences in body fat content and serum leptin concentrations between HF and LF mice, serum leptin levels were positively correlated with body fat mass. These results support the hypothesis that A. chevrieri can compensate the poor-quality diet physiologically by way of increasing food intake and decreasing thermogenesis.
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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) | 0.001 | 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 | 0.001 | 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".