An ecology centre and trail system for the Tsolum River watershed
Bibliographic record
Abstract
The Tsolum River flows through the Comox Valley on Vancouver Island, British Columbia, Canada. A local stream stewardship group, the Tsolum River Restoration Society, has been working to restore the river's once-plentiful salmon population. The Society's director has expressed an interest in developing an interpretive trail system, based at an ecology-centre facility, to link their salmon habitat enhancement projects and explain them to the public. From this idea, the project's scope was expanded by the author, based on the rationale that the trail planning should encompass a broader program. As public trail development opportunities are rare, it was felt that the system should achieve the Society's goals, while providing an enjoyable recreational experience; accessing outstanding cultural and natural features; linking to other routes or trails; and providing an aesthetic experience. This project provides planning and design recommendations for achieving these objectives. Results of the project, including maps, diagrams, plans, and drawings, will be presented and discussed. Suitability analysis, utilizing a Geographic Information System, is the primary method used in data synthesis and trail layout. A theoretical paradigm, the Ecological Aesthetic, and associated methodologies will be discussed, and applications to the project will be described and assessed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.058 | 0.004 |
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".