Manipulating individual state during migration provides evidence for carry-over effects modulated by environmental conditions
Bibliographic record
Abstract
Despite observational evidence of carry-over effects (COEs, events occurring in one season that produce residual effects on individuals the following seasons), to our knowledge no experimental studies have been carried out to explore how COEs might affect reproductive output. We simulated an environmental perturbation affecting spring-staging migrants to investigate COEs in greater snow geese (Anser caerulescens atlanticus). During three consecutive years, 2037 females captured during spring staging (approx. 3000 km south of their Arctic breeding grounds) were maintained in captivity (with or without access to food) for 0-4 days. Duration of captivity (but not food treatment) negatively affected reproductive success, probably through stress response. Reproductive success was reduced by 45-71% in 2 years, but not in a third year with unusually favourable breeding conditions. This unprecedented manipulation indicates that COEs can have a strong effect on individual reproductive success in long-distance migrants, but that this effect can be partly compensated for by good environmental conditions on the breeding ground.
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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 | 0.001 | 0.002 |
| 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) | 0.001 | 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".