Comparing migration ecology among geographically distinct populations of Canada Geese (Branta canadensis) and Cackling Geese (Branta hutchinsii).
Bibliographic record
Abstract
Migration timing is important to the reproductive success of birds, as mismatches with peak food abundance can lead to reduced fitness and population declines. Birds breeding at northern latitudes may be more susceptible to the effects of climate change, as narrower seasonality farther north can result in timing mismatches for birds that may rely more on endogenous cues to migrate. Few studies have used direct-tracking methods on waterfowl to compare differences in migration ecology across a latitudinal gradient. Spatiotemporal tracking data can also be useful for conservation and management of waterfowl. Giant Canada Geese (Branta canadensis maxima) are increasing in numbers, to the point where they are declared overabundant. Special hunting seasons may be opened to increase harvest of this subspecies, but care must be taken to avoid non-target goose populations. My first objective was to use direct-tracking data to examine differences in migration timing and rate between three goose populations: giant Canada Geese, Southern Hudson Bay Canada Geese (B. c. interior), and Cackling Geese breeding across a broad latitudinal range (49-65 degrees). My second objective was to apply my findings in relation to conserving and managing overabundant Canada Geese, and whether spring migration in overabundant Giant Canada Geese and less abundant Cackling Geese overlap with the proposed spring hunting season (March 1 to March 31). I found that southern-breeding geese migrated earlier and with more variation in spring compared to more northern-breeding geese, and in fall the northern-breeding geese migrated earlier compared to more southern-breeding geese. I also found that all three goose populations were in Manitoba during the fall hunting season, and about 9% of Giant Canada Geese were in Manitoba during the proposed spring hunting season. Increasing our knowledge of migration ecology in waterfowl can be useful in conservation of species that may be susceptible to the effects of climate change, or managing species that are increasing in numbers in which timing data can aid decisions to open special hunting seasons to increase harvest rates.
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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 | 0.000 | 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".