Do environmental conditions experienced in early life affect recruitment age and performance at first breeding in common goldeneye females?
Bibliographic record
Abstract
Environmental conditions experienced early in life may have long‐term impacts on life history traits and reproductive performance. We investigated whether ambient temperature experienced during the first two to four weeks of life and weather severity during the first two winters affected recruitment age and relative timing of breeding in the year of recruitment in female common goldeneyes Bucephala clangula . Our sample consisted of 141 female recruits hatched in a study population in central Finland between 1985 and 2013 and captured later as breeders. About 56% of the recruited females bred for the first time when two years old (range 2–6 yr). Individuals facing colder ambient temperatures during the first two to four weeks posthatch or more severe winter conditions during the first two winters did not recruit at an older age. Nor did maternal characteristics, relative hatch date or nest site availability affect recruitment age. For females that recruited at two years old, the date of first breeding was usually late relative to the population mean that year (mean difference 6.9 d, range –7 to 21 d). Our results suggest developmental buffering enables female goldeneye ducklings to mitigate the impacts of adverse environmental conditions experienced during the first weeks of life, at least in terms of first breeding.
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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 | 0.000 | 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".