Mapping the Historical Trails of Rose Swanson Mountain
Bibliographic record
Abstract
In response to growing concerns about the limited accessibility and preservation of historical trails at Rose Swanson Mountain, this study uses aerial photos and GIS technology to digitize these trails. In this work, we expand on this work by examining the historical trails of Rose Swanson Mountain in British Columbia. Our goal is to create a comprehensive and accurate digital map of the trails while examining the impact of logging and forest fires on their length and location. The results indicate that the total length of the trails in Rose Swanson Mountain has significantly decreased due to logging activities and regulations set by the BC Ministry of Forestry. Additionally, we found that most of the existing trails are located within a sensitive area. However, our study also highlights the limitations of using aerial photography for digitizing historical trails, including the difficulty of identifying trails from air photos and the accuracy of the georeferencing process. The digital map of the historical trails can significantly contribute to the preservation of local indigenous culture and promote sustainable forest management practices that consider both economic benefits and ecological benefits. Our findings can guide policymakers in assessing the impact of logging on forests near Canadian indigenous traditional community areas and implementing regulations to mitigate these effects. Furthermore, future studies can explore the benefits of preserving the forest and historical trails, such as the forest's carbon storage potential and the economic contribution of the trail system to the local economy.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".