Microbial processes with the potential to mobilize As from a circumneutral-pH mixture of flotation and roaster tailings
Bibliographic record
Abstract
Abstract The Northwest Tailings Containment Area at the inactive Giant Mine (Canada) contains a complex mixture of arsenic-containing substances, including flotation tailings (84.8 wt%; with 0.4 wt% residual S), roaster calcine wastes (14.4 wt% Fe oxides), and arsenic trioxide (0.8 wt%) derived from an electrostatic precipitator as well as As-containing water (21.3 ± 4.1 mg L −1 As) derived from the underground mine workings. In the vadose zone the tailings pore water has a pH of 7.6 and contains elevated metal(loid)s (2.37 ± 5.90 mg L −1 As); mineral oxidizers account for 2.5% of total 16S rRNA reads in solid samples. In the underlying saturated tailings, dissolved Fe and As concentrations increase with depth (up to 72 and 20 mg L −1 , respectively), and the mean relative abundance of Fe(III)-reducers is 0.54% of total reads. The potential for As mobilization via both reductive and oxidative (bio)processes should be considered in Giant Mine remediation activities. The current remediation plan includes installation of an engineered cover that incorporates a geosynthetic barrier layer.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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 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".