Strength of Laboratory Synthesized Hydrate‐Bearing Sands and Their Relationship to Natural Hydrate‐Bearing Sediments
Bibliographic record
Abstract
Abstract The strength of hydrate‐bearing sediments is an important input parameter for numerical simulations for evaluating the long‐term future gas production from these sediments and the risks associated with such activities on the environment. Studies on laboratory synthesized, and natural, hydrate‐bearing coarse‐grained soils exhibit similar behavior, where increasing hydrate saturation increases specimen strength and stiffness with the corresponding development of peak stress, postpeak strain softening and tendency for sample dilation, which is suppressed with increasing effective stress, although strength is increased. For synthesized specimens, hydrate growth at grain contacts leads to “cementing” behavior and the largest increase in strength, which is subdued when hydrate growth is prevented at these locations. Sample disturbance in natural samples lead to lower strength and stiffness compared to laboratory synthesized samples. Unconfined compression shear tests on natural samples highlight the strong “cementing” effect of gas hydrates on coarse‐grained soils. The strength of natural sediments appears strongly correlated with particle size and clay content, with smaller particle and increasing clay content reducing strength for a given hydrate saturation. The strength parameters, friction angle, and cohesion appear to depend on sample type. For synthesized specimens, friction angle was reasonably independent of hydrate saturation while cohesion increased in an exponential manner, with the largest increase occurring when hydrate growth is at particle contacts. In contrast, friction angle appeared to increase with a corresponding reduction in cohesion for natural hydrate‐bearing sediments. However, these observations may be related to sample disturbance and stress conditions under which the data were acquired.
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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".