Genetic Analysis of Standardized Collections of Cackling and Canada Goose Harvests
Bibliographic record
Abstract
Abstract: Many states have established special harvest seasons and hunting zones to target overabundant populations of temperate‐nesting Canada geese ( Branta canadensis ) and protect less abundant northern‐nesting populations of Canada geese and cackling geese ( B. hutchinsii ). To meet management needs for spatially and temporally explicit harvest estimates, we utilized established methods of genetic stock identification and standardized harvest sample collections to estimate proportional contributions of cackling ( B. h. hutchinsii ) and Canada geese ( B. c. maxima and B. c. interior ) to 5 consecutive annual harvests (1998‐2002) in Michigan, USA. Harvest samples ( n = 2,272) were analyzed for several seasons at statewide, regional, and local spatial scales. We expanded upon previous studies that used genetic methods to monitor cackling goose and Canada goose harvests by analyzing harvests within regional and local goose management areas. Likelihood ratio tests were also employed to compare harvest composition among spatial and temporal sampling groups. Tests revealed that proportions of giant and interior Canada geese within local harvests varied significantly during the fall in different hunt zones of Michigan and during different time periods. Adaptive management of temperate‐nesting and northern‐nesting geese would benefit from accurate estimates of harvest composition, as provided by genetic‐based methods. Quantification of changes in harvest composition as a function of variation in season opening date and duration, and bag limits can provide valuable insight into goose migratory behavior and population dynamics. Harvest composition estimates may be used to predict impacts of management prescriptions on mortality rates of specific breeding populations.
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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".