Matching productivity to resource availability in a small predator, the stoat (<i>Mustela erminea</i>)
Bibliographic record
Abstract
Stoats (Mustela erminea), introduced to New Zealand in the late nineteenth century, are common in New Zealand beech (Nothofagus sp.) forests, where populations of feral house mice (Mus musculus) fluctuate between years much as voles do in the northern hemisphere. We present new field evidence and two models demonstrating (i) a strong correlation between density indices for young stoats in summer and for mice in the previous spring, and (ii) a significant linear relationship between productivity per female and spring density of mice up to 25 mice captures per 100 trap-nights. These models confirm that short-lived small mustelid predators dependent on fluctuating populations of prey have evolved means of matching their productivity to the prospects of success across a wide range, from total failure in rodent crash years to >12 independent young per female in rodent peak years. We suggest that the enhanced reproductive success of female stoats when rodents are abundant is due to a combination of critical improvements in both the reproductive physiology and the foraging behaviour of female stoats in rodent peak years. Conversely, a drastic shortage of rodents increases the mortality of embryos and nestlings, while the adult females are able to survive, and even remain relatively fat, on other foods.
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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 | 0.001 |
| 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".