Evaluation of a single-layer desulphurized tailings cover
Bibliographic record
Abstract
The potential effectiveness for long-term closure of a desulphurized tailings cover placed on reactive tailings at the Detour Lake Mine was assessed. The single-layer cover was aimed at reducing oxygen ingress using high water saturation to limit oxygen diffusion and consumption of oxygen in the cover by residual sulphide minerals. The cover was 1.0 to 1.5 m thick and met desulphurization targets, but was coarser grained than the design. One-dimensional unsaturated flow and oxygen diffusion modelling was used to predict water content profiles, depth of oxygen penetration, and diffusive oxygen fluxes. Effective diffusion and reaction rate coefficients estimated from field data matched laboratory measurements. Depth to water table was identified as the most important factor for reducing tailings oxidation. The deeper (4 m) water tables measured at the cover edges led to a lower degree of saturation and higher oxygen flux. Finer grain size and higher air-entry values in the cover materials helped maintain saturation, and a capillary break (fine over coarse) cover reduced oxygen fluxes to <5% of fluxes to uncovered tailings. The as-built cover (coarse over fine) reduced oxygen influx by at most 50% and as low as <1% of uncovered tailings.
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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".