What waterfowl hunters want: exploring heterogeneity in hunting trip preferences
Bibliographic record
Abstract
Abstract Canadian and American waterfowl hunters were surveyed to identify their hunting trip preferences. Respondents were individuals that were now participating or had participated in waterfowl hunting, and most had hunted the majority of the last five years. We identified four latent classes of waterfowl hunters that varied in their preferences for harvest, access effort, length of travel, quantity of waterfowl seen, and the potential for interference/competition. We found a diminishing return associated with the number of waterfowl harvested, and that ‘devoted’ and ‘local’ hunters did not perceive appreciable benefit from harvesting more birds beyond harvesting a single bird. Results highlight the importance of not only considering population size, but also the location of habitat for people and waterfowl. Our results provide waterfowl managers important insights into the heterogeneity of North American waterfowl hunters by highlighting differences in priorities for waterfowl hunting trips. Notably, to address this heterogeneity, managers could consider the balance of objectives, actions and resources designed to satisfy current waterfowl hunters. Managing access to improve the likelihood that hunters will see and have opportunities to harvest some waterfowl has benefit to hunters.
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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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".