Mineralogical, geospatial, and statistical methods combined to estimate geochemical background of arsenic in soils for an area impacted by legacy mining pollution
Bibliographic record
Abstract
The estimation of geochemical background is complex in areas impacted by point sources of atmospheric emissions due to unknowns about pollutant dispersion, persistence of pollutants on the landscape, and natural concentrations of elements associated with parent material. This study combined mineralogical analysis with conventional statistical and geospatial methods to separate anthropogenically impacted soils from unimpacted soils in the Yellowknife area, Northwest Territories, Canada, a region that was exposed to 60 years of arsenic (As)-rich atmospheric mining emissions (1938–1999) and that hosts natural enrichments of As. High concentrations of As (up to 4700 mg kg−1) were measured in publicly accessible soils near decommissioned roaster stacks in the region and strong relationships between As and distance from the main emission sources persisted in surface soils and soils at depth in the soil profile more than 60 years after the bulk of mining emissions were released. Mineralogical analysis provided unambiguous evidence regarding the source of As minerals and highlighted that most As in surface soils within 15 km of Yellowknife is hosted as anthropogenic arsenic trioxide (As2O3), produced by roaster stack emissions. Statistical protocols for the estimation of geochemical background were applied to an existing database of till geochemistry (N = 1490) after removing samples from mining impacted areas. Results suggested geochemical background for the region is 0.25–15 mg kg−1 As, comparable to global averages, with upper thresholds elevated in volcanic units (30 mg kg−1 As) that often host sulfide mineralization in greenstone belts in the region.
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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 | 0.001 | 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 | 0.001 |
| 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".