The effectiveness of road closures and deactivations at reducing traffic: A case of resource roads used for recreational activities in Ontario, Canada
Bibliographic record
Abstract
We investigated the effectiveness of different approaches at limiting motorized vehicle traffic on unpaved roads designed to support forestry operations (i.e., resource roads). The approaches (i.e., year-round closure, seasonal closure, deactivation, and deactivation and closure) were employed to provide non-road-based opportunities for people to pursue outdoor recreation and nature-based tourism activities by deterring hunters and others from using motorized vehicles to access areas beyond a road closure/deactivation. Using the case of weekend traffic on single-lane resource roads in northern Ontario, Canada during the gun portion of the moose (Alces alces) hunting season, we developed and investigated three general hypotheses. The first hypothesis that closure and/or deactivation approaches significantly reduce traffic on these resource roads was supported. In fact, we estimated that on average these approaches should reduce about 78% of traffic. No support existed for the second hypothesis that the effectiveness of these approaches depends on road quality. The third hypothesis was supported that differences exist in the effectiveness of the four road closure and/or deactivation approaches to reduce traffic on resource roads. A year-round closure was amongst the least while a seasonal road closure was amongst the most effective approach to reduce traffic on these roads.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".