Thin layering in tailings deposits and its implications for CPT-based state parameter estimation
Bibliographic record
Abstract
The cone penetration test (CPT) is used to characterize tailings and infer the state parameter (ψ) using methods calibrated on uniform specimens. This work explores whether subaerially deposited tailings resemble uniform specimens. We quantified layering in tube samples from three active gold tailings storage facilities and visually appraised a 3 m profile at a fourth. Pronounced thin layering was found in every sample and in the exposed profile; the thickest apparently uniform layer was ~15 cm, far below the >0.6 m thickness recommended for ψ estimates. The variability of particle size distribution within a single 25 cm sample frequently matched or exceeded that measured across two entire impoundments of different commodities. At the observed layer thicknesses the cone cannot develop an isolated-layer response, so several layers jointly govern its readings. Thin layering is therefore a clear departure from the uniformity assumption underpinning widely used CPT interpretation methods, and it also obstructs the very testing that would be needed to characterize the resulting error. We conclude that, for subaerially deposited tailings, the confidence currently placed in CPT-based ψ estimates exceeds what the evidence supports. We argue for more space for the characterization of specimens that preserve in situ layering.
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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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".