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
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 in some studies, 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 train's symmetry. We developed a model based on an alternate arrangement of primordial feather buds during development and locations of concentric circles of eyespot distribution using the pattern on the train as a template. Results: We observed a zigzag pattern of feather follicles that determined both the number and the arrangement of eyespots on the train. Our model explains the bilateral symmetry of train feathers, the hexagonal arrangement of eyespots on the train, and 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 feathers as a function of age. Conclusions: We propose a multimodal model of mate choice which holds that (1) eyespot number and feather length are developmentally correlated and females see them not as separate traits but as one complex trait combining both, (2) females may not always choose males with the largest number of eyespots, as old males may lack vigor, and (3) females may choose mates on the basis of train size, vigor, and beauty. The maladaptation of the long tail is a byproduct of the adaptation of the tall train. Who would have thought that zigzag arrangement, the densest form of spherical packing, when applied to the living world would produce such profound effects on phenotypic diversity.
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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 | 0.001 |
| 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".