Survival and Recruitment among Non-Migratory Canada Geese (Branta canadensis): Influence of Gosling Sex, Hatching Date, Mass at Fledging, and Family Type
Bibliographic record
Abstract
Among some Arctic-nesting geese, mass at fledging impacts first-year survival and recruitment into the breeding population, but this might not be true for temperate-nesting geese. For 25 years, we examined what variables impacted survival and recruitment of 731 Canada Geese (Branta canadensis) fledglings raised in Connecticut, USA by tracking individually marked birds throughout their lives. At fledging, the number of females (391) were similar to males (340), but by the end of the first year of life, females (247) outnumbered males (187). This can be explained due to the apparent survival rates for juvenile females (0.63) being higher than for males (0.55). Dispersal rate from natal areas were similar for males and females during the first year of life and cannot account for why females outnumbered males after one year of life. Apparent survival rates of female fledglings to the end of the second year was 0.48. For the 247 females still alive at the end of their first year of life, 190 were still alive at the end of their second year of life, yielding a 0.77 apparent survival rates during their second year of life. Sex was the only variable that explained the survival of fledglings until the end of their first year of life. The probability of females surviving the second year of life was not influenced by hatching date, fledging age, fledging mass, or family type. Heavier females at fledging were more likely to be recruited into the breeding population than lighter ones. Our results indicate that while an inability to acquire sufficient mass as a gosling does not affect survival, it impacts the ability to be recruited.
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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) | 0.000 | 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".