Geochemistry of uranium mill tailings in the Athabasca Basin, Saskatchewan, Canada: A review
Bibliographic record
Abstract
The Athabasca Basin, located in northern Saskatchewan, Canada, is a major source of global U and an important economic driver for the province and country. Athabasca Basin U deposits consist of uraninite and pitchblende dominated by quartz and aluminosilicates and varying amounts of sulfide and arsenide minerals associated with varying concentrations of As, Se, Mo, Ni, and 226Ra (elements of concern; EOCs). Processing these U ores results in tailings that are often enriched in EOCs. Mill treatment processes are designed to generate tailings that minimize the long-term environmental impact of U tailings, although many challenges exist in reaching this goal. Many studies have contributed to our understanding of the geochemistry of these tailings and EOCs and their potential impact on the surrounding hydrosphere. Using nearly two decades of data from tailings samples, mill sampling campaigns, and laboratory experiments, this review provides a comprehensive analysis of the geochemistry and long-term behavior of U tailings in the Athabasca Basin and develops a geochemical model of the tailings. Results of this review are applicable to tailings generated from other milling operations with comparable acid leaching hydrometallurgical processes. This holistic review also highlights the limitations to our current understanding of U tailings geochemistry.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".