On evolving size and shape of gas bubble in marine clay under multi-stage loadings: microcomputed tomography (μCT) characterization and cavity contraction analysis
Bibliographic record
Abstract
Discrete bio-gas bubbles commonly form in fine-grained marine sediments and have modified many aspects of the behavior of these sediments, including strength, stiffness, and permeability. Although the level of such modifications is known to govern by bubble shape and size, limited studies have been undertaken, mainly due to difficulty in nondestructively characterizing bubbles within a soil under in situ stresses. In this study, a mini-loading device was developed to perform one-dimensional loading tests on gassy marine clay and gassy silt in a microcomputed tomography (μCT). The evolving bubble shape, size, and pressure during loading were quantified, and the resulting stress fields around the bubble cavities were evaluated via elliptical cavity contraction analysis considering stress anisotropy. As the vertical load increased, bubble cavities were found to compress predominantly along the vertical loading direction, with little horizontal compression, because localized soil failure (LSF) and thus cavity collapse occurred mainly near the roof of the at-rest lateral earth pressure coefficient (K 0 )-stressed elliptical bubble cavities. The evolution of bubble shape and size under loading is significantly affected by stress anisotropy, which governs the extent and location of the LSF. A Gaussian mixture model is adopted to quantify the evolving distributions of bubble structure parameters, which are essential for developing more physically rigorous gassy soil models.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".