A long tail of truth and beauty: a simple rule of pattern formation explains symmetry, complexity and beauty in the peacock’s tail
Bibliographic record
Abstract
Darwin’s theory of sexual selection by female choice has become a standard explanation for exaggerated sexually dimorphic traits, such as the peacock’s (Pavo cristatus) long tail. Eyespot beauty-based female choice requires genetic variation in female preference and the number of eyespots, as well as a genetic correlation between the two. However, little genetic variation has been documented in either of these traits in natural and feral peacock populations. We examined the anatomical plan underlying feather development and discovered that eyespot feather follicles originate in alternating rows of 10/11, which uniquely determines the train’s feather complexity, bilateral symmetry, and eyespot arrangement and beauty. This pattern precludes intrinsic variation in eyespot number, resulting in a fixed number of total eyespots in fully mature individuals. Since number of eyespots and tail length are independent traits and function of the age of the animal, the only variation available in these trats is also function of age. We propose an alternate, male drive, hypothesis in which females choose males based on their overall dominance (size, vigour, call, courting), and beauty of the train may (or may not) be a factor in female choice but it cannot affect the train length. This hypothesis can explain all known results.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.027 | 0.037 |
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".