Effects of operational sex ratio and male density on size-dependent mating in Minshan’s toads, <i>Bufo minshanicus</i>, on the Tibetan Plateau of China
Bibliographic record
Abstract
Abstract In many animal species, an increase in the operational sex ratio (OSR), density or a combination of both should lead to more intensive competition among individuals of the more abundant sex. To test this, we examined pairing patterns of Minshan’s toad (Bufo minshanicus) from six populations between 2008 and 2015 along the eastern Tibetan Plateau in south-west China. OSRs in breeding aggregations of Minshan’s toad are normally male biased and males actively compete with each other for acquisition and retention of mates. We found evidence that deviations from random mating by size varied between populations and between years according to the magnitude of the OSR and male density. Larger males were generally more successful in pairing than smaller males when the OSR was slightly male biased and male density was high. However, the resulting size-disproportionate mating was more evident when OSR was closer to 1.99, indicating a positive correlation with the intensity of aggressive scramble competition. Thus, the intensity of male-male competition may partly explain variation in size-disproportionate mating among populations.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".