Male reproductive success and female preference in bushy-tailed woodrats (<i>Neotoma cinerea</i>): do females prefer males in good physical condition?
Bibliographic record
Abstract
In many mating systems, females may benefit by selecting a male with high genetic quality in the form of good genes or compatible genes. In bushy-tailed woodrats ( Neotoma cinerea Ord, 1815), previous research has shown that male reproductive success correlates with the mass change of males over the breeding season, indicating that physical body condition may directly influence female choice and hence male reproductive success. We examined male physical condition in relation to reproductive success in the field. Male physical condition was measured as body-mass change over the breeding season, body size, body condition (mass versus size), and anaemia (packed cell volume and mean corpuscular volume). We then conducted trials in the laboratory in which captive females were presented with visual and olfactory cues from two males simultaneously. In the field, males with a low mean corpuscular volume had the highest reproductive success. Captive females also showed a preference for males with low levels of anaemia based on mean corpuscular volume. These results suggest that females are employing a condition-dependent preference.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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) | 0.002 | 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".