Conservation Offsets and Pipeline Construction: A Case Study of the TMX Anchor Loop Project
Bibliographic record
Abstract
When Terasen Pipelines (later Kinder Morgan Canada) sought to loop its Trans Mountain pipeline through Canada’s Jasper National Park and British Columbia’s Mount Robson Provincial Park, both being components of the Canadian Rocky Mountain Parks UNESCO World Heritage site, the company faced formidable regulatory and public interest obstacles. However, the company and several environmental groups agreed not to test the strength of their respective uncertain legal positions, but to work co-operatively with each other and with park managers. The motivating goal was to design into the looping project some aspect of environmental improvement that would result in a net benefit to the ecological conditions of the two parks, more than compensating for the residual disturbance which would be caused by the looping after mitigation. The central concept was that of a “conservation offset” (also known as “biodiversity offset”), which has been defined as: “conservation actions intended to compensate for the residual, unavoidable harm to biodiversity caused by development projects, so as to ensure no net loss of biodiversity.” This paper reviews the history of the discussions and planning which took place, considers the adequacy of the outcomes, and suggest lessons for using conservation offsets as a means to align proponent and stakeholder interests and improve environmental outcomes for linear projects beyond the prospects offered by mitigation alone.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".