Determinants of growth rates and mass of Canada geese goslings
Bibliographic record
Abstract
ABSTRACT Fledgling mass is an important determinant of first‐year survival and recruitment into the breeding population for many Arctic‐nesting goose species. In turn, fledgling mass of these geese is influenced by hatch date and forage quality and quantity in the brood‐rearing area. Less is known about the determinants of growth rates and fledgling mass in temperate‐nesting geese. For 25 years, we examined near‐fledging mass, hatch dates, and growth rates of Canada geese (Branta canadensis) nesting in Connecticut, USA by tracking individually marked geese that wore neck collars or large plastic leg bands. Fledgling mass was influenced by sex (males were heavier), gosling age when weighed, and family type (fledglings raised in 2‐parent families were heavier than those raised in gang broods). Paternal nesting experience (years of prior nesting) influenced fledgling mass, probably because goslings with experienced fathers hatched earlier than goslings with inexperienced fathers. Among fledglings raised in gang broods, fledgling mass was positively correlated with the number of parents attending the brood and negatively correlated with the number of goslings within the brood. Gosling growth rates (daily gain in mass) were higher for males than females; goslings in 2‐parent families grew 2 g/day faster than those in gang broods. Late‐hatched goslings grew faster than goslings that hatched earlier. In gang broods, growth rates were positively correlated with the ratio of parents to goslings. Assessing goose sex ratios, Julian hatching dates, family types, and brood sizes will allow waterfowl managers in temperate regions to refine their goose population models. This, in turn, will allow waterfowl managers to determine more accurately what proportion of the populations can be safely harvested or how best to manage nuisance goose populations. © 2018 The Wildlife Society.
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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) | 0.001 | 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".