In situ equilibrium pore-water pressures derived from partial piezoprobe dissipation tests in marine sediments
Bibliographic record
Abstract
Excess pore-water pressure has a significant effect on submarine slope stability and sediment deformation, and therefore its in situ equilibrium measurement is crucial in carrying out accurate slope stability assessments and accurately deriving geotechnical design parameters. In situ equilibrium pore-water pressure is usually obtained from pore pressure decay during piezocone tests. However, submarine shelves and slopes are often characterized by the existence of low-permeability (fine-grained) sediments involving long dissipation tests that are an important issue for offshore operational costs. Consequently, short-term and (or) partial dissipation tests are usually performed and in situ equilibrium pore-water pressures are predicted from partial measurements. Using a modified cavity expansion approach, this paper aims to predict for four different sites the in situ equilibrium pore-water pressures. Comparisons between predicted and observed in situ equilibrium pore-water pressures allowed the development of a guide to evaluate the minimum time required to perform short-term dissipation tests for a given marine sediment. The main finding of this Note is that the second derivative of the pore pressure, u, versus the logarithm of time, t, ∂2u/∂ln(t)2 must be positive to calculate accurately the in situ equilibrium pore-water pressures from partial measurements.
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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 | 0.000 | 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) | 0.000 | 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 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".