Comparison of stormwater quality metal impacts from residential, commercial, and industrial catchment areas in Grande Prairie
Bibliographic record
Abstract
Non-point source pollution from stormwater runoff is a growing issue for global surface water sustainability. Baseline water quality data from Bear Creek indicate metal contamination increases as the stream flows through the City of Grande Prairie in northwest Alberta. This research measured metal concentrations entering Bear Creek from six stormwater catchments to compare if and how different land uses affect metal contamination. Water quality and quantity data were analyzed and determined that heavy industrial land uses contribute more significant metal concentrations in stormwater discharge than light industrial, residential, or commercial areas. These results, along with analysis of development characteristics, suggest that mitigation should focus on industrial catchments that feature gravel land cover. It is recommended that the City implement engineered controls such as wet ponds and administrative controls such as frequent street cleaning in industrial areas with gravel land cover to reduce non-point source metal pollution entering Bear Creek.
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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 | 0.001 |
| 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".