Data from: Translocation experiment of taiga bean geese Anser fabalis provides evidence for oblique social learning of moult migration
Bibliographic record
Abstract
While there is ample evidence supporting genetic control of migratory behaviour in short-lived passerines, long-lived social species have been assumed to rely solely on cultural inheritance of migratory routes. Evidence from experimental studies supporting this idea is scarce. We tested whether the moult migration in taiga bean geese Anser fabalis has an inherited component or whether the birds need oblique social learning (where knowledge on migration is transferred from any experienced individual to any naïve individual conspecific) to carry out this journey. In many waterfowl species, non-breeders and failed breeders migrate to remote places for wing moult while successful breeders stay at the breeding grounds and moult with their chicks. We translocated one-year-old taiga bean geese before their first moult migration to sites outside of the breeding range to examine whether they display innate moult migration behaviour without experienced conspecifics or not. The birds were equipped with GPS-transmitters and released in randomly assigned groups of two. Wild control one-year-old birds were released immediately after capture with other non-breeding geese, while a procedural control group consisting of older birds was held in captivity until released at the same time with the translocated one-year-old birds but in the place where they were captured. Most translocated birds found conspecifics and either joined locally moulting breeders or followed experienced birds to moulting sites in Russia. Two of the translocated birds did not find other bean geese and settled to moult together in SW Finland. The wild control birds moult-migrated as expected, while only one of the procedural control birds moult-migrated to Russia and the remaining three stayed with locally moulting breeders in Finland. Our results support the idea that moult migration in geese is culturally inherited, highlighting the importance of the non-relative, experienced adult individuals have in maintaining population-specific behaviours.
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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 | 0.000 | 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) | 0.002 | 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".