Factors Influencing American Woodcock Hunter Satisfaction in Canada
Bibliographic record
Abstract
From 1991 to 2005, we surveyed American woodcock (Scolopax minor; hereafter, woodcock) hunters in 3 Canadian provinces to assess hunter satisfaction. Across all submitted reports, 42.0% of the respondents reported a ‘poor’ experience, 35.2% of the hunters reported an ‘average’ experience, and 22.1% of the hunters reported a ‘good’ experience. We analyzed hunter satisfaction rate with an ordered logistic regression that included province, Singing Ground Survey Population Index (SGS index), number of woodcock harvested, hunting effort (hours hunted), environmental conditions before and during the nesting and brood-rearing periods (i.e., prior to the hunting season), precipitation during the post-fledging period, and year as explanatory variables. We also included a random effect for each individual hunter, to account for repeated answers, and for year, to account for short-term irregular perturbations in hunter satisfaction. Hunters from Nova Scotia were on average more satisfied than hunters from Ontario. Hunter satisfaction was positively correlated with the SGS index and the number of woodcock harvested by the hunter during a hunting trip. Hunter satisfaction was negatively correlated with the amount of precipitation during the nesting period and positively correlated with the amount of precipitation during the post-fledging period. However, there was considerable variation in individual hunter response, with 27.7% of the hunters more satisfied than average and 22.8% less satisfied than average. In fact, the individual hunter response accounted for approximately 75.0% of the variability observed in the model, indicating that accounting for hunter satisfaction would require further investigation. In the meantime, promoting woodcock habitat conservation in southern Canada could increase woodcock populations, harvest opportunity, and, by extension, hunter satisfaction.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".