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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".