Woodland caribou (Rangifer tarandus) avoid wellsite activity during winter
Bibliographic record
Abstract
Woodland caribou (Rangifer tarandus) are threatened in Alberta in part due to the development of oil and gas resources. To inform best management practices for caribou, we assessed how proximity to wellsites influenced caribou habitat selection, and whether habitat selection varied across wellsite activity phases (i.e., drilling, producing, and inactive). We used location data from 37 GPS-collared caribou monitored between 2007 and 2013 in west-central Alberta to model habitat selection. Our results suggest the influence of wellsites on caribou habitat selection are temporally dynamic. The largest impacts occur when human activity at wellsites is greatest, however wellsites continue to influence caribou habitat selection after human activity ceases. Caribou avoided wellsites, and avoidance increased relative to the degree of activity at the nearest wellsite. During early winter, caribou avoided wellsites in the drilling phase more than inactive and producing wellsites. During late winter, caribou avoided wellsites in producing phases more than inactive wellsites. Caribou may benefit from management practices that include i) seasonal timing restrictions on drilling, ii) reductions of human activity at wellsites, whether in duration or intensity, iii) land-use planning to coordinate the placement of wellsites to minimize impacts to caribou and their habitat, and iv) prompt and effective restoration of wellsites to match original habitat conditions once production has stopped.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".