Investigating Factors that Generate and Maintain Variation in Migratory Orientation: A Primer for Recent and Future Work
Bibliographic record
Abstract
The amazing accuracy of migratory orientation performance across the animal kingdom is facilitated by the use of magnetic and celestial compass systems that provide individuals with both directional and positional information. Quantitative genetics analyses in several animal systems suggests that migratory orientation has a strong genetic component. Nevertheless, the exact identity of genes controlling orientation remains largely unknown, making it difficult to obtain an accurate understanding of this fascinating behavior on the molecular level. Here, we provide an overview of molecular genetic techniques employed thus far, highlight the pros and cons of various approaches, generalize results from species-specific studies whenever possible, and evaluate how far the field has come since early quantitative genetics studies. We emphasize the importance of examining different levels of molecular control, and outline how future studies can take advantage of high-resolution tracking and sequencing techniques to characterize the genomic architecture of migratory orientation.
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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 | 0.016 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.007 | 0.021 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".