SPATIOTEMPORAL HETEROGENEITY OF GREATER SNOW GOOSE HARVEST AND IMPLICATIONS FOR HUNTING REGULATIONS
Bibliographic record
Abstract
Changes in harvest rate over the past 3 decades have been shown to be closely related to population growth of greater snow geese (GSG; Chen caerulescens atlantica). We used band-recovery and harvest survey data from 1970 to 2001 to study temporal variations in geographic harvest distribution and composition of GSG in Québec, Canada and the Atlantic Flyway states (AF) in the United States. We sought to determine whether (1) geographic variation in harvest was associated with temporal trends in total harvest rates observed during this period; (2) spatiotemporal distributions of harvest varied with age and sex; and (3) harvest distribution and composition differed between the spring conservation harvest initiated in 1999 and the regular fall hunt in Québec. We detected over time a gradual spreading in the geographic distribution of the fall harvest from the upper St. Lawrence estuary toward southwestern Québec. During winter, a sudden northward shift in the distribution of the United States harvest in the mid-1980s was associated with a high concentration of geese in mid-Atlantic Flyway states (Maryland, Delaware, and New Jersey), possibly due to short-stopping during migration. We argue that this led to a reduction of hunting pressure on GSG and may have contributed to the sudden decline in harvest rate that occurred at that time and ultimately to the ensuing population increase. We observed a decreasing proportion of juveniles in the kill throughout the hunting season within fall staging grounds in Québec and between Québec and the United States. We also found a much higher proportion of adults in the spring harvest than in the fall that is consistent with the conservation goal of increasing the adult harvest. We recommend that management actions focus on increasing harvest in mid-Atlantic Flyway states if further control of population growth is desired.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| 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".