The characterization of arsenic mineral phases from legacy mine waste and soil near Cobalt, Ontario
Bibliographic record
Abstract
The Cobalt-Coleman silver (Ag) mining camp has a long history of mining dating back to 1903. Silver mineralization is hosted within carbonate veins and occurs in association with Fe-Co-Ni arsenide and sulpharsenide mineral species. The complex mineralogy presented challenges to early mineral processing methods with varying success of Ag recovery and a significant amount of arsenic (As) in waste material which was disposed in the numerous tailings deposits scattered throughout the mining camp, and in many instances disposed of uncontained. The oxidation and dissolution of As-bearing mineral phases in these tailings and legacy waste sites releases As into the local aquatic environment. Determining the distribution of primary and secondary As mineral species in different legacy mine waste materials provides an understanding of the stability of As. Few studies have included detailed advanced mineralogical characterization of As mineral species from legacy mine waste in the Cobalt area. \nAs part of this study, a total of 28 samples were collected from tailings, processed material near mill sites and soils from the legacy Nipissing and Cart Lake mining sites. The samples were analyzed for bulk chemistry to delineate material with strongly elevated As returned from all sample sites. This sampling returned highly elevated As with up to 6.01% As from samples near mill sites, 1.71% As from tailings and 0.10% As from soils. From the samples with elevated As, eight samples representative of the different sampling sites and material were selected for detailed mineralogical characterization using scanning electron microscopy (SEM) in conjunction with automated mineralogy using mineral liberation analysis (MLA). Common primary As-bearing minerals identified include sulpharsenides and arsenides such as cobaltite, arsenopyrite, gersdorffite, safflorite and skutterudite; with common secondary As-bearing minerals forming post-processing including erythrite-annabergite, Fe-Ca arsenates and Fe-oxides with As. This characterization study highlighted the localized variability of mineralogical speciation and variations in the abundance of these species from the different sampling sites. The majority of the samples were dominated by secondary As minerals forming from the dissolution and oxidation of primary As-bearing mineral species and occurring as rims on grain particles, grain cementation and replacement of primary minerals. A single sample from fine-grained water saturated tailings is distinctly dominated by primary As-bearing minerals which highlights the localized redox conditions controlling oxidation. The results of this work, as well as previous characterization studies improves the understanding of the mineralogical characteristics and distribution of As-bearing legacy mine waste material with As which is critical to understand the stability of As and improve remediation planning and design.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| 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".