Macrogeographic variation in the song of the Mourning Warbler (<i>Oporornis philadelphia</i>)
Bibliographic record
Abstract
Studies of macrogeographic variation in birdsong involve populations incapable of interbreeding because of physical barriers or separation by large distances. Different patterns have emerged from these studies such as (i) little or no variation exists among individuals or populations from the breeding range, (ii) individual variation is greater than among population variation resulting in no geographic structure, (iii) clinal variation, and (iv) macrogeographic variation where all individuals from several populations on the breeding range share a common song type forming a regional dialect or regiolect. I studied macrogeographic variation in song of the Mourning Warbler ( Oporornis philadelphia (A. Wilson, 1810)). The observed pattern was similar to the fourth category of geographic variation with regiolects. A Western regiolect extended from northern Alberta to western Ontario. An Eastern regiolect stretched eastward from western Ontario and Wisconsin to the Gaspé Peninsula and New England, then southward through the Appalachians to West Virginia. Nova Scotia and Newfoundland each had unique regiolects. Finally, I compared these results to other species with regiolects and assessed the ability of some deterministic hypotheses to explain song divergence (e.g., role of morphology, physical barriers, island isolation).
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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 | 0.001 | 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".