Migration tracking assists harvest management of overabundant Canada geese in Manitoba, Canada
Bibliographic record
Abstract
Abstract Similar to other jurisdictions across North America, populations of temperate‐nesting giant Canada geese (Branta canadensis maxima) have increased to unprecedented levels in Manitoba, Canada, causing extensive damage to crops and property, posing risks to human safety, and mostly avoiding mortality risk from fall hunting seasons. As a result, this population in southern Manitoba was declared overabundant in 2019, through an amendment to the Canada Migratory Birds Regulations. Management authorities sought to increase harvest rates by introducing an additional hunting period in spring, or conservation season, wherein harvest could be concentrated on this population. An important consideration was the need to avoid additional harvest on non‐target populations that do not meet the criteria to be classified as overabundant, specifically, sub‐arctic nesting Canada geese (Branta canadensis interior) and mid‐continent cackling geese (Branta hutchinsii hutchinsii). To investigate differences in migration timing of populations, and ultimately determine start and closure dates for the spring conservation season, we deployed light‐level geolocators on adult females of each population between 2016 and 2018. We used Bayesian approaches to derive locations from light‐level data and generalized linear mixed models to investigate potential differences in timing of international border crossing among goose populations. Migration data confirmed that movement of these populations overlapped extensively in fall, but in spring, a narrow period existed where mainly the target population was present. Our findings provide the basis for decisions about timing of spring conservation seasons for overabundant temperate‐nesting Canada geese in Manitoba, the first season of its kind for this population in North America. Additionally, our work represents the first large‐scale use of light‐level geolocator technology for assisting with waterfowl harvest management decisions.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".