Ecological models provide the first evidence of increased costs for hybrids in a migratory divide
Bibliographic record
Abstract
Ecological speciation predicts that the fitness of hybrids will be reduced if they exhibit intermediate phenotypes that fall between parental niches. Empirical support for this prediction is sparse and migratory divides may help fill this gap. Divides occur between populations with divergent migratory routes. Hybrids in divides are predicted to take intermediate routes over terrain avoided by pure forms, reducing their fitness. We test this prediction here in a well-characterized divide between Swainson's thrushes using niche models and models of landscape connectivity. These models predicted lower habitat suitability in the intermediate range between the migratory ranges of pure forms and optimal routes that circumvent this area. Birds that took intermediate routes used stopover sites of lower predicted suitability and overlapped less with optimal paths than birds migrating on either side of the divide. Our results have broad implications as migratory divides are common in nature and not limited to birds.
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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 | 0.001 |
| 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 | 0.001 | 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".