Influence of test conditions and soil properties on the geomechanical response of hydrate-bearing sands
Bibliographic record
Abstract
Testing of natural hydrate-bearing sands (HBSs) exhibits differences in their geomechanical behavior, both in terms of stiffness and shear strength, which may result from differences in hydrate saturation, confining stresses, along with minor differences in particle size distribution (PSD) between the sands. Results from laboratory studies on synthesized hydrate-bearing specimens have sought to consider the effect of PSD, hydrate saturation, and confining stresses on the geomechanical behavior of HBS, although these were limited in scope. Therefore, to gain better insights into this inter-relationship, and the influence it may have on the geomechanical behavior of HBS, this paper reports on a more detailed series of tests conducted on laboratory-synthesized HBS specimens. Tests were carried out using a specially designed triaxial apparatus including a resonant column drive head, which enabled detailed characterization of the evolving geomechanical properties of the sand specimen during the formation process. Observations suggest that the geomechanical response of the specimens is inherently related to the hydrate morphology that develops, and its distribution. These, in turn, are dependent on initial water saturation and suctions that are developed prior to hydrate formation, which are functions of soil properties and initial test conditions such as water saturation and applied stress state.
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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 | 0.001 |
| 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) | 0.001 | 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".