Compressibility and permeability of sand–silt tailings mixtures
Bibliographic record
Abstract
Microstructure showing the involvement of the fine and coarse grains in the soil skeleton is evaluated. Incremental loading tests using a stress-dependent permeameter are conducted on the mixtures of poorly graded sand and nonplastic fines originating from tailings. The results are compared with the published data of various tailings. It is shown that increasing the fines content from 0% to 100%, the involvement of the fine and coarse components of soil skeleton can be classified into four categories: no fines involvement (<10% fines), fines partially involved (10%−35% fines), increasing cushioning effect surrounding the coarse (35%−40% fines), and constant cushioning effect (>40% fines). At the same consolidation stress, the void ratio, e, rapidly decreases for fines less than 30%, then almost remains constant between 30% and 50% fines, and gradually increases for fines exceeding 50%. The hydraulic conductivity, k, decreases more than 20-fold as the fines content increases from 12% to 50%, then remains constant. k is proportional to [e 3 /(1 + e)] A and inversely proportional to S 2 , where A is a factor describing the effect of particle angularity and S is the specific surface. Finally, the influence of fines content on the seepage-induced internal stability is discussed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".