First‐order description of the mechanical fracture behavior of fine‐grained surficial marine sediments during gas bubble growth
Bibliographic record
Abstract
Bubbles in sediments, imaged via Computed Tomography (CT) scanning, and in surrogate transparent material (gelatin), are well‐described geometrically as eccentric oblate spheroids. While sediments are undoubtedly visco‐elasto‐plastic solids, only part of that complex behavior appears to influence significantly the formation and shape of gas bubbles. Specifically, the shape of these bubbles can be explained if the mechanical response of fine‐grained sediment is approximated by Linear Elastic Fracture Mechanics (LEFM). To determine the adequacy of the LEFM approximation for gas bubble growth in fine‐grained sediments, a number of gas bubbles were injected and grown in natural sediments, while monitoring the size and shape using an industrial CT scanner. A comparison of measured inverse aspect ratios (IARs) of the injected bubbles with calculated IARs from pressure records provides support for the LEFM theory. Deviations from LEFM are observable in the data, but as bubbles grow larger they trend more closely toward the theory. The use of LEFM has been shown to describe gas bubble growth in shallow coastal sediments to first order.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".