Mechanical compaction behaviour of natural clays and implications for pore pressure estimation
Bibliographic record
Abstract
In pore pressure estimation and in basin modelling programs it is often assumed that porosity in clay-rich sediments depends on the vertical effective stress. An alternative assumption is that porosity depends on the mean effective stress. Yet triaxial test data on natural clays have shown that porosity depends on both the mean effective stress and the differential stress. Triaxial test data for Winnipeg clay are re-plotted here to quantify the errors in estimated pore pressures that would result if it is assumed that porosity depends on either vertical or mean effective stress. The assumption that porosity depends on vertical effective stress may result in gross underestimates of pore pressures in compressional basins, where horizontal stresses in overpressured zones at depth are greater than the vertical stress. Sections through the yield surface of Winnipeg clay are consistent with a generalized yield locus for clays, normalized for composition as well as volume, based on data from several natural clays. Consequently, a refined equivalent depth method of pore pressure estimation that accounts for porosity dependence on both mean and effective stress could, in principle, be implemented. The method would require some knowledge of yield properties and of all three principal stresses in the subsurface.
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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 | 0.001 | 0.009 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".