Incorporating the value of watchable wildlife in the landuse \nplanning process: values and impacts of British Columbia's bear-viewing industry
Bibliographic record
Abstract
We examined the economic impact of commercial grizzly bear viewing in British Columbia and the potential impact it could have on the new land-use planning process. Surveys of operators described economic rents attributed to bear viewing and identified barriers to success and positive elements within the industry. We incorporated a tourist motivation questionnaire to describe the importance of bear viewing on choice to visit the region and province in order to accurately allocate visitor expenditures. Responses support the presence of a wildlife-viewer tourist typology. Responses from bear viewing operators were not sufficient to enable a full industry economic analysis but were adequate for the creation of a set of parameters for future planning of grizzly bear viewing operations in the province, as is required by the land-use planning process. A map of possible grizzly bear viewing locations on the coast was produced and compared to known biodiversity values and presence of old growth forest, with a discussion on the potential commercial bear viewing has to preserve high value landscape. Mean values of bear viewing operations indicated they were worth the equivalent of 1290 Hectares of Oldgrowth forest when measured on a simple financial basis. Legislative, operational and other barriers to success are discussed in the context of expanding the commercial grizzly bear viewing industry.
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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 | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".