Characterisation of mineral forms of arsenic in garden soils from a historic gold mining region
Bibliographic record
Abstract
Soils in the Yellowknife region were contaminated with arsenic by >50 years of atmospheric mining emissions from ore roasting operations. Persistent community concern regarding contamination of local garden soils prompted this investigation. One hundred and fifteen soil samples were collected from 110 resident gardens and were analysed by inductively coupled plasma mass spectrometry for elemental analysis. Arsenic concentrations were below local remediation soil quality guidelines for residential areas (160 mg kg −1 ) in all soil samples, but 62 % of samples exceeded national Canadian soil quality guidelines (12 mg kg −1 ) for the protection of environmental and human health. Ten samples, with relatively high arsenic concentrations, were analysed by scanning electron microscope with automated mineralogy to identify solid phase arsenic hosts; four samples were further analysed by synchrotron-based microanalysis to identify crystal structure of target mineral grains. The predominant mineral host of arsenic in the garden soils was identified as arsenopyrite , which could be geogenic, anthropogenic (e.g., repurposed mine waste), or both. Arsenic trioxide from ore roaster stack emissions was identified in five garden soils mineralogically analysed. Arsenic-bearing iron oxides were detected in nine of the soils mineralogically analysed; in three of these soils, roaster-generated iron oxides generated by ore roasting were identified. Garden soil arsenic concentrations (14 mg kg −1 median) were substantially lower than values determined by a previous study for undisturbed, Public Health Layer soils in the region (390 mg kg −1 median); likely, mixing of surface soils with soil beneath, and use of purchased soils in gardening has dispersed the surficial arsenic enrichment consequent of ore roasting.
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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 | 0.001 | 0.001 |
| 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 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".