Assessment of slurry consolidation using index properties
Bibliographic record
Abstract
Several engineering applications involve dilute slurries such as mine tailings, municipal sludges, dredged materials, and hydraulic fills. An assessment of the consolidation behavior is pivotal for minimizing the environmental footprint of these fine-grained slurries at the outset of deposition. The volume compressibility and hydraulic conductivity properties during dewatering are governed by complex solid–liquid interactions that are captured by the index properties. This study focused on analyzing slurry consolidation in conjunction with soil consistency. Results indicated that the consistency limits for sedimentary clays covered a wide range due to the presence of various clay minerals. Sesquioxide coating of clays and dominance of non-clays resulted in lower liquid and plastic limits for residual soils. The relatively high limits for oil sand tailings were due to residual bitumen that imparts thixotropy and lubrication. The analyzed consolidation data (σ′ = 1 kPa to 450 kPa, e = 10 to 1, and k = 10−4 to 10−9 cm s−1) for each material group fitted best to a decreasing power law for compressibility and an increasing power law for conductivity. The 99% confidence intervals were wide at low σ′ and high e and gradually narrowed down with a change in these parameters. The volume compressibility coefficients (A and B) linearly varied with Ip whereas the hydraulic conductivity coefficients (C and D) followed decreasing power law functions of Ip. Strong agreements between measured and modeled data were obtained for both void ratio (R2 = 0·98) and hydraulic conductivity (R2 = 0·99).
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".