Mine reclamation enhances habitats for wild ungulates in west‐central Alberta
Bibliographic record
Abstract
Surface mining is the most prevalent form of coal extraction in North America. Reclamation aims to transform former surface mines into self‐sustaining ecosystems that support uses similar to predevelopment conditions. Success of reclamation often is determined by assessing the re‐establishment of landscape structure and vegetation communities. However, there is increasing interest in evaluating reclamation success in the context of higher trophic levels. We evaluated response to mining and reclamation by sympatric bighorn sheep ( Ovis canadanesis ), elk ( Cervus elaphus ), and mule deer ( Odocoileus hemionus ) on reclaimed coal mines in west‐central Alberta. We used direct ground counts on a fixed survey route to obtain data on abundance and distribution of ungulates during 2004 to 2017. We created a grid of 200 × 200 m grid cells and assigned each group of ungulates to a grid cell. We assigned landscape and topographic features to these grid cells to represent changes due to mining and reclamation. We estimated resource selection functions for bighorn sheep, elk, and mule deer showing how their use of reclaimed features and landscapes increased access to quality forage and decreased predation risk. Ungulates also responded to mining and reclamation in ways that we did not anticipate, e.g. sheep and elk often selected areas near haul roads. Understanding spatial relationships between reclamation prescriptions and higher trophic levels is important when designing “bottom up” reclamation to restore ecological functions including recruitment of wildlife.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 teacher head, 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".