Do gravel highways affect water quality and invertebrate communities in Arctic lakes?
Bibliographic record
Abstract
Gravel roads are a common feature in developed areas of Canada’s Arctic. These roads can be a source of calcareous dust that drifts to roadside lakes, causing significant changes in conductivity, calcium, and pH levels. In this study, we examined if road proximity was associated with differences in water quality and invertebrate communities in lakes along the Dempster and Inuvik-Tuktoyaktuk Highways in the Northwest Territories, Canada. We collected biological and water quality data from 18 lakes selected using a stratified random sampling design, with distance from the road (0–300 m, 300–600 m, and >600 m) and region of study (boreal forest, tundra) as the two factors. We hypothesized that lakes closer to the road would exhibit differences in water quality and invertebrate communities associated with road dust pollution and other stressors caused by roads. We found no clear differences in water quality or invertebrate communities among lakes based on distance from the highways. In addition, while there were differences between regions, these did not appear to be related to the effects of the roadways. Our results suggest that variability in lake morphometry and water quality in this region might be more important than the influence of roads.
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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 | 0.000 | 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 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".