Geostatic stress in oil-sand tailings
Bibliographic record
Abstract
Horizontal geostatic stress estimated from self-bored pressuremeter (SBP) data using the ‘lift off’ method is uncertain because of even small deficiencies in self-boring, but that uncertainty can be minimised by modelling the complete test. Iterative forward modelling based on large-strain cavity expansion in frictional dilating (non-associated Mohr–Coulomb) soil, with correction for finite SBP geometry, is both easily implemented in a spreadsheet and computes quickly. Such modelling of a campaign of SBP tests in oil-sand tailings shows a baseline geostatic stress ratio K0 = 0.6 for those tailings that are truly normally consolidated. Other geological history factors, including compaction by tracking and wetting–drying cycles, adds about a Δσh ≈ 60 kPa ‘locked-in’ stress to this normally consolidated trend; an alternative view is that these factors produce K0 ≈ 1. The modelling spreadsheet is provided as a downloadable Excel application in the online supplementary material.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".