Does Dust from Arctic Mines Affect Caribou Forage?
Bibliographic record
Abstract
This study explores how dust from the Ekati Diamond Mine potentially affects the availability and quality of forage on the seasonal range of the Bathurst caribou herd. Understanding the effects of dust as a source of disturbance is important because the Bathurst caribou population has declined by 93% since the middle 1980s and there are reports that caribou in general may avoid mining projects. There are several challenges for quantifying dust impacts: 1) Natural variations (e.g., topography, natural disturbance, and soil pH) may also impact forage availability and quality for caribou. To minimize their masking effect, we stratified survey sites into seven land cover classes and selected the most populous class (i.e., the dwarf shrub) for assessing the impact. 2) Within class variation (e.g., the proportion of area covered by rocks where vascular plants and lichen do not grow) can further skew the analysis. We eliminated this problem by examining only the area not covered by rocks. 3) Coarse and fine suspended particulates have different spatial coverages, chemical compositions, and pH values. Consequently, their impacts on caribou forage can be different. To distinguish their impacts, we sampled two areas: transects from the Misery Haul Road that has been in active use vs. those from a rarely used spur road outside the Misery Camp. We sampled percent vegetation cover, soil pH, and dust on leaves along these transects during the summers of 2015 and 2016. Our results indicated that the amount of dust on leaves in a zone of ~1000 m from the Misery Haul Road was 3 - 9 times than that of background sites. The zone of reduced lichen percent cover was also about 1000 m. In contrast, these road dust-induced changes in caribou forage were not observed for the dust-free transect from the spur road.
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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 | 0.001 |
| 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 | 0.001 | 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".