Song discrimination by male Mourning Warblers (<em>Geothlypis philadelphia</em>) and implications for population divergence across the breeding range
Bibliographic record
Abstract
Geographic variation in song may reduce or eliminate the ability of some populations to recognize each other as conspecifics, possibly leading to assortative mating, reproductive isolation, and speciation. Song playback experiments, used to evaluate the significance of geographic variation in song, have been particularly useful in discovering divergence among previously unknown populations of sibling species. In this study, I report the results of song playback to male Mourning Warblers (Geothlypis philadelphia) from populations throughout the breeding range and discuss the implications for population divergence. Four regions in the breeding range contain unique song types or regiolects: western, eastern, Nova Scotia, and Newfoundland. Results of reciprocal song playback experiments showed that males from the western and Newfoundland regiolects respond more aggressively to songs in their own regiolect than those in the other regiolects. Interior populations, i.e., eastern and Nova Scotia regions, showed little or no difference in aggressive response toward their own versus other regiolects. This pattern may be due to a combination of geographic proximity of populations belonging to different regiolects, song learning, experience, and contact during migration. Song discrimination by populations from the western Prairie Provinces and Newfoundland is consistent with the existence of at least partial reproductive isolation at the geographic extremes of the breeding range.
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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 | 0.000 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".