Decreased litter size in inactive female mink (<i>Neovison vison</i>): Mediating variables and implications for overall productivity
Bibliographic record
Abstract
Meagher, R., Bechard, A., Palme, R., Díez-León, M., Hunter, D. B. and Mason, G. 2012. Decreased litter size in inactive female mink ( Neovison vison ): Mediating variables and implications for overall productivity. Can. J. Anim. Sci. 92: 131–141. Farmed mink vary dramatically in activity: very inactive individuals rarely leave the nest-box, while others spend hours active daily, often performing stereotypic behaviour (SB). SB typically correlates with increased reproductive output, and inactivity, with decreased output. Our objectives were to determine whether SB or inactivity best predicted litter size (LS), and to test three hypothesized reasons for inactive dams’ reduced LS: H1, excess fat; H2, chronic stress (potentially underlying inactivity because fear motivates hiding); and H3, health problems. We assessed time budgets pre-breeding, scored body condition visually, conducted health exams, and assessed stress using faecal cortisol metabolites (FCM) and "glove tests" for fear. Results did not support H2 and H3: inactive females were no more fearful than active females (P>0.10), they excreted lower levels of FCM (P=0.033), and were considered healthy. As predicted by H1, inactive females had higher body condition scores (P<0.0001), which predicted decreased LS (P=0.040). However, path analysis determined this was unlikely to mediate the inactivity–LS relationship. Compared with SB, inactivity more consistently predicted both LS (negatively, P ≤ 0.038) and kit weight (positively, P ≤ 0.037). Therefore, decreasing inactivity in farmed mink, rather than increasing their SB or decreasing their body condition should most improve productivity.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".