Assessing the Benefits of Extra-pair Mating for Female Purple Martins (Progne subis)
Bibliographic record
Abstract
Approximately 75% of socially monogamous passerines pursue extra-pair mating with the frequency of extra-pair paternity varying among and within taxonomic groups. Despite the ubiquity of extra-pair mating systems, substantial research into the subject has produced mixed results and the benefits to females remain elusive. Two genetic benefits hypotheses, the good genes hypothesis and heterozygosity theory, predict that extra-pair offspring (EPO) should generally be more fit than within-pair offspring (WPO). This study aims to test for genetic-based benefits to extra-pair mating in purple martins (Progne subis) by comparing EPO and WPO. Specifically, I compare the first year survival estimates of EPO and WPO and of those offspring that are recruited into the breeding population, I compare the reproductive success of EPO and WPO. I found no differences in first-year survival probability nor did I find any differences in reproductive success between EPO and WPO. I conclude that female purple martins are not benefiting from extra-pair mating through the improved survival or reproductive success of their offspring. Such benefits may be context-dependent or historical contexts in which the benefits of extra-pair mating for females may no longer exist for this semi-domesticated species.
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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 | 0.000 | 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".