Migration patterns and habitat use by molt migrant temperate‐breeding Canada geese in James Bay, Canada
Bibliographic record
Abstract
Numbers of temperate‐breeding Atlantic and Mississippi Flyway Canada geese have greatly increased since the 1980s. Consequently, numbers of yearlings, sub‐adults and failed breeders undertaking pre‐molt migration to northern latitudes has also increased, potentially providing additional hunting opportunities for Cree hunters living near James Bay, Canada. We described movement patterns and habitat use of molt migrant Canada geese Brenta canadensis maxima along the east coast of James Bay based on nine geese fitted with GSM–GPS devices during 11 northward and eight southward migrations between 2015 and 2019. Geese staged for 2.8 ± 0.6 days (mean ± standard error of the mean) at 3.2 ± 0.6 staging sites (mostly tidal flats and salt marshes) from the first week of June in spring and 3.8 ± 1.8 days at 2.0 ± 0.5 staging sites (mostly inland freshwater wetlands, peatlands and tidal flats) from the first week of September when returning south. Shallow and deep water habitats were used as resting sites during both migrations. In spring, molt migrants were mostly harvested in June well after the migration of sub‐arctic breeding geese whereas in autumn, both subspecies were harvested in September. Molt migrant temperate‐breeding geese can increase harvest opportunities and represent supplementary wildlife food for Cree communities. However, the current number of molt migrant geese harvested by Cree hunters is not sufficient to significantly impact populations of temperate‐breeding geese.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".