Signaling Individual Identity versus Quality: A Model and Case Studies with Ruffs, Queleas, and House Finches
Bibliographic record
Abstract
We develop an evolutionary model that predicts that characters selected to signal individual identity will have properties differing from those expected for indicator signals of quality. Traits signaling identity should be highly variable, often display polymodal distributions, not be condition dependent (i.e., be cheap to produce and/or maintain), not be associated with fitness differences, exhibit independent assortment of component characters, and often occur as fixed phenotypes with a high degree of genetic determination. We illustrate the existence of traits with precisely these attributes in the ornamental, conspicuously variable, and sexually dimorphic breeding plumages of ruff sandpipers Philomachus pugnax and red-billed queleas Quelea quelea. Although ruffs lek and queleas are monogamous, both species breed in high-density aggregations with high rates of social interactions (e.g., aggression and territory defense). Under these socioecological conditions, individual recognition based on visual cues may be unusually important. In contrast to these species, we also review plumage characteristics in house finches Carpodacus mexicanus, a nonterritorial, dispersed-breeding species in which plumage ornamentation is thought to signal quality. In keeping with expectations for quality signals, house finch plumage is relatively less variable, unimodally distributed, condition dependent, correlated with fitness measures, has positively correlated component characters, and is a plastic, environmentally determined trait. We briefly discuss signals of identity in other animals.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".