Canada Goose Gosling Mortality During Prefledging and Early Migration on Akimiski Island, Nunavut
Bibliographic record
Abstract
Abstract Mortality rates for precocial avian species are highest during their first year of life. For harvested species, including waterfowl, it is particularly difficult to determine causes of mortality using standard mark–recapture techniques. The timing and cause of nonhunting mortality is difficult for researchers to distinguish, and therefore, difficult to incorporate into management decisions. We marked flightless Canada goose Branta canadensis interior goslings with radio transmitters to determine the timing of mortality prior to harvest during the fall migration. We tracked survival of 206 goslings for 2 mo from an average of 50 d of age to just prior to the fall migration departure from the breeding grounds in 1999 and 2000. The most parsimonious model from a set of candidate models describing probability of survival showed clear differences between years and suggested that survival was affected by gosling size (largest survived better) and hatch timing disparity. We indexed hatch timing disparity by measuring the difference between gosling hatch date and the date of the estimated peak in forage quality as determined by the normalized difference vegetation index. The latest hatched goslings presumably had the lowest quality forage available to them. Both size and hatch timing disparity concur with a mortality cause that is most likely from complications due to malnutrition. The estimated survival probability of goslings to 51 d post marking was 0.31 (95% confidence limit = 0.22 to 0.44) in 1999 and 0.69 (95% confidence limit = 0.57 to 0.83) in 2000. Managers can make better informed decisions concerning the utility of harvest regulation in the conservation of juvenile Canada geese by better understanding the timing, causes, and annual variation of their mortality.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".