Manipulating the appearance of a badge of status causes changes in true badge expression
Bibliographic record
Abstract
Signals of dominance and fighting ability (i.e. status signals) are found in a wide range of taxa and are used to settle disputes between competitive rivals. Most previous research has considered status-signal phenotype as an attribute of the individual; however, it is more likely that signal expression is an emergent property that also incorporates aspects of the social environment. Furthermore, because an individual's signal phenotype is likely to influence its social interactions, the relationships between status signals, social environment and individual quality are probably much more complex than previously appreciated. Here, we explore the dynamic relationship between social interactions and signal expression in a previously undescribed status signal, the frontal shield of the pukeko (Porphyrio porphyrio melanotus: Aves). We demonstrate that frontal shield size is a strong predictor of dominance status within social groups, even after controlling for potentially confounding variables. Then, we evaluate the relationship between social interactions and signal expression by testing whether manipulating apparent shield size influences (i) dominance interactions and (ii) future signal expression. By showing that decreasing apparent shield size causes both an increase in the amount of aggression received and a decrease in an individual's true shield size, we provide the first evidence of dynamic feedback between signal expression and social interactions. Our study provides important insight into the role of receiver-dependent (i.e. social) costs in maintaining signal honesty and demonstrates a unique approach to studying status signalling applicable to future studies on dynamic morphological signals.
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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 | 0.000 |
| 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 | 0.001 |
| Research integrity | 0.000 | 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".