Does prey density predict characteristics of primiparity in a solitary and specialized predator, the Canada lynx (<i>Lynx canadensis</i>)?
Bibliographic record
Abstract
Age at primiparity is a flexible life-history trait that purportedly responds to changing population dynamics and variable resource abundance. We examined placental scars in yearling Canada lynx (Lynx canadensis (Kerr, 1792)) from the island of Newfoundland and used pregnancy rates and litter sizes to indicate primiparity. We modelled these lynx productivity data with snowshoe hare (Lepus americanus Erxleben, 1777) population attributes using seven multiple a priori competing hypotheses. Hare abundance showed peak, decline, and increase phases, and densities based on capture–mark–recapture estimates ranged from 0.11 to 1.19 hares·ha−1. Overall, yearling pregnancy rate was 23.5% and the model with hare abundance fitted alone had the most support. However, surprisingly hare abundance explained little (6%) variation in yearling pregnancy rate. Mean (±SE) litter size was 3.51 ± 0.27. None of our covariate models provided unequivocal support for predicting yearling litter size. We speculate that individuals may exhibit behavioural plasticity such that they can dampen the impact of primary prey abundance on yearling pregnancy rate by exploiting alternate prey. Furthermore, intraspecific social interactions may provide additional insight into the determinants of pregnancy rate in yearling lynx.
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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 | 0.002 |
| 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 | 0.000 |
| 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".