Effects of dietary fat:carbohydrate ratio on nutrient digestibility, serum parameters, and production performance in male silver foxes (<i>Vulpes vulpes</i>) during the winter fur-growing period
Bibliographic record
Abstract
Forty male silver foxes were used to investigate the effects of increasing dietary fat:carbohydrate (F:C) ratio {34:34, 41:28, 48:22, and 55:17 [metabolizable energy basis (ME)]} on growth performance, nutrient digestibility, serum parameters, and pelt quality during the winter fur-growing period. The results showed that average daily feed intake, average daily ME intake, average daily gain (ADG), feed efficiency, and protein digestibility were improved (P < 0.01 or P < 0.05) when dietary F:C ratio ranging from 34:34 to 48:22. However, foxes that received the 55:17 feed had lower (P < 0.05) ADG and protein digestibility than the other groups. The fat digestibility was increased (P < 0.01), and the carbohydrate digestibility was decreased (P < 0.01) with the increasing dietary F:C ratio. In addition, serum triglyceride and low-density lipoprotein cholesterol significantly increased (P < 0.05) as dietary F:C ratio increased. Changing dietary F:C ratio from 34:34 to 48:22 resulted in an increase in pelt quality, but it had negative effects on growth and pelt quality when foxes received 55:17 feed. We conclude that the optimal dietary F:C ratio for silver foxes during the winter fur-growing period was 48:22.
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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) | 0.001 | 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".