ASSESSING ROAD DUST IMPACTS ON MACROINVERTEBRATES COMMUNITY STRUCTURE IN CANADIAN ARCTIC LAKES
Bibliographic record
Abstract
In the Canadian Arctic, unpaved gravel roads are essential for regional accessibility but are also potential sources of road dust runoff. My study investigated the impacts of road dust from the Dempster and Inuvik-Tuktoyaktuk Highways on adjacent freshwater ecosystems, focusing on water quality parameters and macroinvertebrate communities. Using a stratified random sampling design, 18 lakes were studied across two regions (boreal forest and tundra) and three distance categories from the road (0-300 m, 300-600 m, and > 600 m). Contrary to my initial hypotheses, findings revealed no significant differences in water quality or invertebrate communities relative to distance from the road. However, differences were noted in dissolved nitrogen and dissolved organic carbon levels between boreal and tundra lakes, as well as in macroinvertebrate community composition. Dust trap experiments confirmed dust dispersal up to at least 300 meters from the road, with higher deposition in tundra areas. The discrepancy between dust movement and lack of observable impacts on lakes suggests that other factors, such as lake morphometry, watershed characteristics, and regional variability, may overshadow potential road dust effects. My study highlights the intrinsic complexity of Arctic freshwater ecosystems and emphasizes the need for long-term, multi-seasonal studies to better distinguish between anthropogenic influences and natural variability.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 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) | 0.000 | 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".