Pairing context determines condition-dependence of song rate in a monogamous passerine bird
Bibliographic record
Abstract
Condition-dependence of male ornaments is thought to provide honest signals on which females can base their sexual choice for genetic quality. Recent studies show that condition-dependence patterns can vary within populations. Although long-term association is thought to promote honest signalling, no study has explored the influence of pairing context on the condition-dependence of male ornaments. In this study, we assessed the influence of natural variation in body condition on song rate in zebra finches (Taeniopygia guttata) in three different situations: during short and long encounters with an unfamiliar female, and within heterosexual mated pairs. We found consistent individual differences in male directed and undirected song rate. Moreover, body condition had a positive effect on song rate in paired males. However, male song rate was not influenced by body condition during short or long encounters with unfamiliar females. Song rate appears to be an unreliable signal of condition to prospective females as even poor-condition birds can cheat and sing at a high rate. By contrast, paired females can reliably use song rate to assess their mate's body condition, and possibly the genetic quality. We propose that species' characteristics, such as mating system, should be systematically taken into account to generate relevant hypotheses about the evolution of condition-dependent male ornaments.
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 | 0.001 |
| 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) | 0.001 | 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".