Moose hunters' perceptions of forest harvesting.
Bibliographic record
Abstract
Ecosystem management takes into account all components of ecosystems, including people. In this context, an improved knowledge of moose (Alces alces) hunters' preferences and perceptions is a prerequisite to the implementation of ecosystem management in the boreal forest. In Quebec, they are one of the most important and one of the most influential groups of outdoor recreationists. In an area subject to intensive forest harvesting in Northwestern Quebec, >90% of hunters interviewed (n = 188) identified camaraderie, presence of a natural environment, and high moose density as the most important criteria in determining the location of hunting areas. Over 60% of hunters though that forest harvesting systems used within their hunting areas were inappropriate. Hunters wanted restrictions on size of cutovers, increased proportion of residual forest, adaptation of cutovers into landscape features, reduction in woody debris, and increased width of forested buffer strips along lakes, watercourses, and around hunting camps. Few differences were noted between hunters with or without cuts within their hunting areas or between hunters in vehicles and those who hunted from camps, suggesting that hunters' perceptions were also influenced by sociological parameters external to the hunting experience. Satisfaction with respect to the hunting experience depended upon the number of moose seen and killed, age of hunters, and presence of cuts within the hunting areas. These results are interpreted in the context of forest ecosystem management.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".