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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".