Male mating success is related to body condition and stress-induced leukocyte response in an anuran with scramble competition
Bibliographic record
Abstract
Competition among males can be a decisive factor for successful mating in anuran explosive breeders with a male-biased population sex ratio. The Yungas Red-belly Toad (Melanophryniscus rubriventris (Vellard, 1947)) is an explosive breeder where males exhibit a classic scramble competition. We studied the body size, body condition, and stress-induced leukocyte response of M. rubriventris males under natural scenarios of scramble competition. We hand-captured adult males (categorised as single, in pairs, or in mating balls) and determined their body mass and length, body condition, and their leukocyte profile. We found that males in pairs had significantly higher body mass than single males and those in mating balls. We found the highest percentage of individuals with better body condition within the paired males who showed significantly higher body condition index value than singles or those in mating balls. Amplectant males (those collected in pairs or in mating balls) had a significantly lower proportion of lymphocytes and higher neutrophil–lymphocyte ratios than singles. Amplectant males showed significantly higher proportions of neutrophils or eosinophils than singles. Our results suggest that M. rubriventris males with better body condition can be more successful in acquiring mates, although scramble competition among males can trigger a stress response.
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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 | 0.001 | 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) | 0.002 | 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".