The influence of forested landscape attributes on hunter site choice and participation
Bibliographic record
Abstract
This work illustrates the use of a non-timber valuation model linked to a number-of-trips prediction model to examine the significance of various forestland attributes and hunter characteristics on hunting site choice and participation in the Foothills Model Forest in west-central Alberta. Data were obtained through a survey that featured determination of hunting trip locations over a three-year period. Clearcut density, fire density, two access variables and, in particular, habitat suitability for ungulate game species were found to be significant variables that influenced hunter site choice. Welfare estimates determined by the number-of-trips prediction model highlight the applicability of the linked model in presenting a more complete picture of the effects of two landscape change scenarios. These scenarios show how the models could be used by land managers to balance economic and social benefits in a bid to move towards sustainable development of forest resources. Key words: non-timber valuation, revealed preference, habitat suitability, random utility theory, landscape attributes, hunter behaviour, multinomial logit
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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 | 0.002 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".