Size-resolved analyses of trace elements in snow from an open-pit bitumen mining and upgrading region
Bibliographic record
Abstract
Dust containing potentially toxic trace elements (TEs) from open pit mining, smelting of metallic ores, aggregate extraction, and road dust is a major concern worldwide. The potential ecological significance of TEs in these dusts, however, depends not only upon their concentrations, but also their physical and chemical forms. Here, dusty snow from the Athabasca River (AR) which bisects an open-pit bitumen mining and upgrading area in Canada was collected to perform size-resolved analysis of selected TEs. Conservative, lithophile (Al, Th, Y), bitumen-enriched (Mo, Ni, V), and chalcophile (As, Cd, Pb, Sb, Tl) elements were overwhelmingly found in the particulate fraction (>0.45 μm), with concentrations increasing toward industry. The mineralogical composition of this fraction was similar to dusts from natural and anthropogenic sources in the area. In the "filterable" fraction (<0.45 μm), Al, Mo, and V in snow were elevated near industry. Within the filterable fraction, TEs occur predominantly in the "truly dissolved" fraction (<300 Da): these are assumed to be ionic species and small molecules, and represent potentially bioavailable species. However, the concentrations of TEs in this fraction were extremely low: for perspective, Cd and Pb are similar to values reported for ancient Arctic ice. Within the filterable fraction at midstream sites, up to 30% of Ni and 37% of Y were associated with organic colloids (≈1 kDa) which may be from bitumen and soil-borne sources, respectively. Except for V, TE concentrations in the filterable fraction of snow were below the average values for the AR and the global average for uncontaminated river water. Consequently, the threat to aquatic life in the river by TEs in snowmelt may be limited.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".