General Properties of Commingled Materials for Mix Designs
Bibliographic record
Abstract
Tailings volumes continue to collectively increase worldwide, leading to larger dams and tailings management facilities. With numerous high-profile dam failures in the past decade, the risks of these management practices are also growing. A potential shift to waste management practices at mineral mines is to commingle waste rock and dewatered tailings. This blended material should have superior physical strength properties provided by the waste rock together with improved chemical stability characteristics associated with the low permeability of the tailings. Ideally, commingled tailings and waste rock can be used to construct various mined earth landforms that are both physically and chemically stable, which will enhance operational performance and ultimately provide for the sustainable decommissioning and closure of the mining facility. To study these materials, the University of Alberta Geotechnical Centre is working with global industry partners to test commingled materials from several mine sites with varying ore and host rock types and climate regimes. The first stage of this study is described here and is focused on the optimum density, saturated hydraulic conductivity, and soil–water characteristic curves of various blend ratios, performed at laboratory scale.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.019 | 0.005 |
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".