Supplemental data for: Finding Mr. Right: Housing quality affects male mouse attractiveness to females, with implications for conservation captive breeding
Bibliographic record
Abstract
Females generally prefer mates with traits indicating low stress (e.g. large size; good health). In captivity, stress from suboptimal housing might therefore reduce male attractiveness. We tested this hypothesis using mice (Mus musculus), predicting that compared to males conventionally-housed (CH) in small lab cages, females will preferentially court higher welfare males from well-resourced (WR) ‘enriched’ conditions. First, a small-scale pilot opportunistically used 12 DBA/2 males and 22 DBA/2 females, both sexes differentially-raised (half CH, half WR), but with heterogeneous reproductive experience. In T-maze mate choice tests, CH females preferred males from WR housing, spending significantly more time near and sniffing them. Next, the main study used 12 dyads of DBA/2 male litter-mates, differentially-raised from 4 to 12 weeks old. Twelve virgin oestrous DBA/2 female sister pairs were each presented with a dyad of differentially-raised virgin brothers. Choice behaviour was videoed, and analyzed blind to housing. Ultrasonic recording confirmed courtship singing in all trials. Females spent on average 1.5 times longer near males from WR housing than their CH brothers, sniffing them for nearly twice as long: a significant preference that did not vary with female housing. Effects were not explained by male body weight, testis weight or anogenital distance. Overall, as predicted, WR housing rendered males more attractive. When subsequently housed for mating in male-sister-pair trios, 6 WR and 6 CH, WR trios also tended to be more likely to have litters. For species with problematic breeding programmes, improving welfare via higher-quality housing may therefore help improve reproductive success.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.775 | 0.212 |
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".