Common vole (<i>Microtus arvalis</i>) population sex ratio: biases and process variation
Bibliographic record
Abstract
Vole population sex ratio varies seasonally. However, population sex ratios have usually been estimated using naïve estimators that do not allow for biases owing to the sex difference in capture probabilities and movement distances (i.e., effective areas sampled). Here we aimed to advance the methodological approach, recognizing that there are two different classes of contributing mechanisms to the pattern which are best addressed separately: (1) those mechan isms imposing a systematic error (bias) in population estimates and (2) those generating the true process variation. Analyzing 7-year capture–recapture data in the common vole (Microtus arvalis (Pallas, 1778)), we quantified both types of biases and revealed that the bias owing to differential capture rates was often severe and less predictable, whereas that owing to differential effective areas was smaller and overestimated male numbers for most of the year. We demonstrated unambiguously that the unbiased population sex ratio indeed varies seasonally, with the males usually being more numerous over winter and spring. By testing predictions from two mechanistic hypotheses to explain the process variability, we found evidence for both the differential recruitment hypothesis and the differential survival hypothesis. From April–May to August, it was the females that were recruited more to the population and that had higher survival rates than males. We suggest that the seasonal variation in the population sex ratio is not merely a result of biasing mechanisms but an important population property driven by the joint effect of differential recruitment and differential survival between sexes.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".