Geometallurgical ore characterization of the high-grade polymetallic unconformity-related uranium deposit
Bibliographic record
Abstract
ABSTRACT Cigar Lake is a polymetallic, unconformity-related uranium deposit with complex geochemistry and mineralogy located in the eastern Athabasca Basin of northern Saskatchewan, Canada. Variable concentrations and spatial distributions of elements of concern, such as As, Mo, Ni, Co, Se, and Zr, associated with the high-grade tetravalent uranium ores [UO2+x; U(SiO4)1–x(OH)4x] present unique mining, metallurgical, and environmental challenges. Sulfide and arsenide minerals have significant control over As, Mo, Ni, Co, and Se abundances and have properties that affect element of concern mobility, thus requiring consideration during mineral processing, mine-effluent water treatment, and long-term tailings management. The U-bearing (uraninite, coffinite) and metallic arsenide (nickeline, often called “niccolite” in the past), sulfarsenide (gersdorffite, cobaltite), and sulfide (chalcopyrite, pyrite, galena, bornite, chalcocite, sphalerite, pyrrhotite) minerals provide the main controls on the distributions of the elements of concern. Arsenic, Ni, and Co occur primarily in a reduced state as 1:1 molar ratio, Ni-Co:As, arsenide, and sulfarsenide minerals such as gersdorffite, nickeline, and cobaltite. Molybdenum occurs within molybdenite and uraninite. Selenium occurs within coffinite, sulfide, and sulfarsenide minerals. Zirconium is found within detrital zircon and coffinite. The spatial distribution and paragenesis of U-, As-, and S-bearing minerals are a result of the elemental composition, pH, and redox conditions of early formational and later meteoric fluids that formed and have modified the deposit through access along lithostratigraphic permeability and tectonic structures. Using the holistic geometallurgical paradigm presented here, the geochemistry and mineral chemistry at Cigar Lake can be used to optimize and reduce risk during long-term mine and mill planning.
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 | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".