Multi-modal, interrelated navigation in migratory birds: A data mining study
Bibliographic record
Abstract
Understanding how long-distance migratory birds navigate remains challenging, particularly in how they integrate multiple environmental cues. Traditional studies, primarily based on laboratory experiments and displacement studies, may not capture the complexity of navigation in the wild. In this study, we applied a data mining approach to investigate the navigational strategies of greater white-fronted geese ( Anser albifrons ) during their annual migrations between the Arctic and central Europe. We integrated a decade of tracking data from 117 individuals with high-resolution geomagnetic data (including solar-wind–induced variations), wind conditions, and a potential visual cue. Hierarchical cluster analysis revealed multi-modal and interrelated navigation strategies that flexibly adapted to environmental conditions such as wind, diurnal cycles, and flock-specific dynamics. Under favourable tailwinds, geese maintained stable headings with minimal changes in geomagnetic heading and apparent angle of geomagnetic inclination, consistent with both geomagnetic loxodrome and magnetoclinic routes, enabling efficient flights towards stopovers or simultaneously towards stopovers and final destinations. Geese also appeared to combine visual landmarks with geomagnetic information, adjusting their reliance on landmarks between day and night. Our findings highlight the complexity and adaptability of avian navigation and emphasise the role of multi-modal sensory integration and environmental context in shaping migratory decisions.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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 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".