View from a canoe: Modelling wilderness canoeists' perceptions and preferences for Northern Ontario's boreal landscape
Bibliographic record
Abstract
Canoe paddlers' campsite and route preferences as well as landscape perceptions of pristine and disturbed landscapes in northern Ontario are solicited in a novel internet survey.The survey instrument combines elements of the Scenic Beauty Estimation (SBE) and two discrete choice experiments (DCE).The analysis consists of a separate scenic beauty model, a campsite selection model, and a route choice model.The best fitting route choice model combines the route choices with scenic beauty evaluations and the campsite choice model in one sequentially nested logit model.Scenic beauty ratings are affected by several biophysical and contextual factors, including forest class and landscape disturbance level.The route choices are influenced strongly by forest type, minimum SBE, and campsite quality.Finally, management implications of these findings are discussed.For example, canoeists are very sensitive to human disturbances in the form of buffers, but are more accepting of water crossings.support of my supervisors and the faculty and staff in the School of Resource of Environmental management was also essential to the success of this project.Wolfgang, you have been a wonderful mentor to me.The breadth of experience that I've gained worlung on all of our various projects includmg this one will stand me in good stead in the years to come.Thank you for your continual guidance on this project, and for always keeping the big picture in mind.Len, my thanks go out to you for championing this project with the OMNR.You insight into the modeling aspects of h s project has been invaluable as has your attention to the details.Peter, thank you for bringing your perspective to this paper.Thanks to Rhonda and Bev for always being able to solve any problem I brought to you.Thank you to Laurence for always having the solutions to my technologd difficulties
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".