Project Recommendations for the Kinder Morgan Canada Trans Mountain Legacy Fund
Bibliographic record
Abstract
The goal of this work was to identify and prioritize projects that address key ecological issues in Jasper National Park (JNP) and Mount Robson Provincial Park (MRPP) in a transparent manner that will satisfy stakeholder concerns. In JNP, ecological issues refer to human activity in the valley bottoms of the Miette and Athabasca watersheds. In MRPP, ecological issues concern the Fraser River Watershed. These projects will be recommended for funding by the Kinder Morgan Canada Trans Mountain Legacy Fund. The Yellowhead Highway (Highway 16) and the Canadian National Railway (CNR) line travel east–west through both parks. Other human developments in the valley bottoms include utility lines and oil and gas pipeline right-of-ways. Wildlife mortality along travel corridors is a significant management issue recognized by MRPP and JNP. Current traffic volumes along Highway 16 and CNR may deter animals from approaching or crossing the transportation right-of-way. The barrier effects of these features are expected to increase as traffic volumes grow with the expansion of coastal ports and urban growth. Population persistence of large and wide-ranging animals increases with access to habitat. Therefore, movement across roads can be an important component for the survival of many species. Developing pro-active approaches to restore connectivity across corridors in the near future will allow management to mitigate or minimize the effects of anticipated increases in traffic.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.130 | 0.025 |
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".