Putting the environmental impact assessment process into practice for woodland caribou in the Alberta Oil Sands Region
Bibliographic record
Abstract
Since 1985, woodland caribou (Rangifer tarandus caribou) have been designated as a threatened species in Alberta. Populations studied since the 1970s have been stable or declining, with no population increases documented. Resource expansion into previously undeveloped areas and associated increases in access have been implicated as possible causes for the declines. To facilitate development on caribou ranges, while ensuring the integrity and supply of caribou habitat, standing committees have been formed. The primary role of the committees is to act as advisory bodies to the government and to search for effective and efficient industrial operating guidelines. Recent research has been conducted on the responses of woodland caribou ecotypes to increased human and predator access. Based on this research, operating guidelines have been refined and implemented through Caribou Protection Plans. I discuss how the current operating guidelines are put into practice and linked to the Environmental Assessment process within the Oil Sands Region of Alberta. In particular, I discuss the origination of impact predictions, specific mitigation measures to reduce impacts and monitoring.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.037 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".