Genomic architecture of migration timing in a long-distance migratory songbird
Bibliographic record
Abstract
Abstract The impact of climate change on spring phenology poses risks to migratory birds, as migration timing is controlled predominantly by endogenous mechanisms. Despite numerous studies on internal cues controlling migration, the underlying genetic basis of migration timing remains largely unknown. We investigated the genetic architecture of migration timing in a long-distance migratory songbird (purple martin, Progne subis subis ) by integrating genomic data with an extensive dataset of direct migratory tracks. Our findings show migration has a predictable genetic basis in martins and maps to a region on chromosome 1. This region contains genes that could facilitate nocturnal flights and act as epigenetic modifiers. Additionally, we found that genomic variance explained a higher proportion of historic than recent environmental spring phenology data, which may suggest a reduction in the adaptive potential of migratory behavior in contemporary populations. Overall, these results advance our understanding of the genomic underpinnings of migration timing and could provide context for conservation action.
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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 | 0.001 |
| 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) | 0.002 | 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".