Call divergence is correlated with geographic and genetic distance in greenish warblers (<i>Phylloscopus trochiloides</i>): a strong role for stochasticity in signal evolution?
Bibliographic record
Abstract
Divergence in signalling systems might play a central role in speciation. To assess the importance of possible causes of signal divergence, we examine two types of vocalizations within a geographically variable species complex, the greenish warblers (Phylloscopus trochiloides Sundevall). Calls, which are used by both sexes throughout the year, and songs, which are sung primarily by breeding males, differ distinctly between two distinct Siberian forms. Through a ring of southern populations that connect the northern forms, signal divergence is correlated with both geographic distance and genetic divergence. Calls and songs differ in their particular patterns of geographic variation, probably because of the larger influence of sexual selection on songs than on calls. These patterns are supportive of neither acoustic adaptation nor morphology being major drivers of divergence in vocalizations. Rather, these results support the importance of stochastic evolution of communication systems in the evolution of new species.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".