Bibliographic record
Abstract
Reclamation of fluid fine tailing (FFT) storage facilities to their pre-disturbance equivalent landforms is hampered because micrometer size fines, whose surface-area-to-volume ratio is remarkably high, are occupied with siloxane and hydroxy groups, which bind water strongly. The purpose of this study is to differentiate the forms of water physically distributions in FFT and determine their propensities for dewaterability under compressive stresses. Two thermal and two mechanical methods were used to analyze water distributions in FFT. Dynamic and isothermal thermogravimetric analyses of FFT gave a transition from predominately bulk water to coevolution with water of higher enthalpy of vaporization at 81% (w/w) solids. Differential scanning calorimeter studies were used to determine the non-freezable water amount, with the premise that water that does not freeze by −30 °C is also unlikely to be removable by compressive stresses encountered in tailing treatment processes. The solid weight percent of FFTs with the non-freezable water was 79.6%. A 1D finite-strain model simulation using the constitutive relations of void ratio and effective stress, void ratio, and hydraulic conductivity show that deep-pits filled with such clayey-silt FFT will consolidate to a maximum solids content of 74% (w/w). For separation by centrifugation, the solids content plateaued to a mean of 74% (w/w) for total centrifugal force of ≥30 mega Newtons. These solid contents represent upper thresholds and demonstrate dewatering limit property of an FFT under compressive stresses.
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 | 0.000 | 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".