THE DESIGN OF A BEAUTIFUL WEAPON: COMPENSATION FOR OPPOSING SEXUAL SELECTION ON A TRAIT WITH TWO FUNCTIONS
Bibliographic record
Abstract
Male fiddler crabs, genus Uca, have one greatly enlarged claw with which they court females and threaten and fight other males. Longer claws are more effective signals but are thought to be less effective weapons because the relative closing force at the tip of the claw decreases with claw length. We studied claw morphology and fighting in Uca terpsichores and Uca beebei and found a mechanism that may resolve opposing selection for signaling and fighting ability. When males fought they delivered gripping forces not at the tips but at the tubercles on the inner margins of their claws' fingers. As claws grow, these tubercles remain relatively close to the apex of the gape. Consequently, the mechanical advantage that governs the forces that can be delivered at these tubercles decreases only slightly with increasing claw length allowing the claw to be an effective signal and a powerful weapon. Animal weapons are exceptionally diverse in form and detail of armature and the causes of this diversity are poorly understood. We suggest that the designs of weapons may often reflect compensatory patterns of growth and placement of armature that enhances the weapon's overall utility for multiple uses in competition for mates.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".