An Approach for Characterizing Arsenic Sources and Risk at Contaminated Sites: Application to Gold Mining Sites in Yellowknife, NWT, Canada
Bibliographic record
Abstract
Yellowknife, Canada has an extensive soil arsenic contaminant problem as a result of 60 years of gold mining activity. Multivariate statistics (PCA) were used to determine that the natural concentration of arsenic in the area is also elevated (up to 150 ppm). As total arsenic concentrations may overestimate the actual risk posed to ecological and human health, sequential selective extraction (SSE) and a simulated gastric fluid extraction (GFE) were used to assess environmentally available and bioavailable fractions in soils. Subjecting various soil types to these techniques confirmed this hypothesis. The high arsenic content in crushed mine rock was neither environmentally available (<10% for SSE) or bioaccessible (<12% for GFE). Conversely, the low arsenic content in organic soils is more environmentally available (10-50% for SSE) and bioaccessible (>30% for GFE). Collectively, these techniques can be used to identify actual risks and develop effective remediation strategies.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 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".