A long tail of truth and beauty: A zigzag pattern of feather formation determines the symmetry, complexity, and beauty of the peacock’s tail
Bibliographic record
Abstract
<ns4:p> Background Darwin assumed that the peacock’s long train was maladaptive and was the indirect effect of selection by female mate choice based on the train’s beauty. While a relationship between the feathers’ elaborate features and mating success has been shown, what features of the train females are attracted to remains controversial. Methods We used museum specimens to examine the anatomical plan underlying feather development responsible for the symmetry of the train. We developed a model based on an alternate arrangement of primordial feather buds during development and locations of concentric circles of symmetric eyespot distribution using the pattern seen on the train as a template. Results We observed a zigzag pattern of feather follicles that determined both the number and the hexagonal arrangement of eyespots on the train. Our model explained not only the alternate arrangement of feathers on the train but also the arrangement of the concentric color rings of the eyespots. While the zigzag pattern explains the symmetry, complexity, and structural beauty of the peacock’s train, it also precludes variation in eyespot number except by annual addition of new rows of feathers as a function of age. Conclusions Since eyespot number and feather length are developmentally correlated and an asymptotic function of a male’s age, their effects on female choice would be confounded and inseparable, and male vigor would be a crucial factor affecting male fitness. Females may not always choose males with the largest number of eyespots, as older males may lack vigor. We propose a multimodal model of female choice <ns4:italic>where females see eyespot and train size not as separate traits but as one complex trait combining both.</ns4:italic> The new model may be able to explain conflicting results and why eyespot number alone may not be sufficient to explain female choice. </ns4:p>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".