Cyclic behaviour of reconstituted and desiccated–rewet thickened gold tailings in simple shear
Bibliographic record
Abstract
A simple-shear apparatus (Norwegian Geotechnical Institute (NGI) type) has been used to investigate the mechanical behaviour of gold tailings under monotonic, cyclic, and post-cyclic loading. Specimens were prepared either by reconstituting the tailings at different water contents or by employing a “dry–wet” preparation technique. The latter technique attempted to simulate a realistic stress history for thickened tailings deposition by preparing the specimen at the pumping water content, and subsequently allowing it to settle and then desiccate to a target water content. Subsequently, these specimens are rewetted and then consolidated. Cyclic resistance ratios for specimens prepared by the dry–wet preparation technique were considerably higher than those of specimens prepared by the standard technique, though there appears to be no advantage in drying the tailings past the shrinkage limit. In general, the tailings could experience significant deformation during cyclic and post-cyclic loading irrespective of whether the monotonic response at a given stress state was contractive or dilative. Shear strength normalized with consolidation pressure, and cyclic resistance ratios, can be plotted as unique functions of void ratio. Post-cyclic strengths require considerable strain to be mobilized. It is recommended that site-specific tolerable strain criteria be adopted to design thickened gold tailings stacks to resist earthquake loading.
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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 | 0.000 |
| 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 | 0.000 | 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".