Estimating Pore Space Hydrate Saturation Using Dissociation Gas Evolution Measurements: In Relevance to Laboratory Testing of Natural or Artificially Synthesised Hydrate-Bearing Soil Specimens
Bibliographic record
Abstract
Physical properties of gas hydrate-bearing soils are known to be greatly affected by the pore space hydrate saturation. The accuracy of most saturation estimation methods is affected by hydrate growth habit and pore space distribution. We highlight the usefulness of dissociation gas evolution measurement (DGEM) method as a reference laboratory method to calibrate most other methods. The DGEM method is based on the concept of mass balance and properties of volume compatibility between two distinct states of a closed system. The accuracy of the estimation depends on (1) the precision with which the laboratory measurements of temperature, pressure, and volumetric properties are obtained (2) and the ability of theoretical models used in the calculation to closely represent the true nature of the system. We perform an analysis to evaluate the sensitivity of the estimation (1) to various laboratory measurements and (2) to the use of different theoretical models to generate a feel for the appropriateness of various assumptions associated with DGEM. The comprehensive guide to available resources useful in the hydrate saturation estimation also serves as one of the major contributions of the work presented.
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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 | 0.002 |
| 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 | 0.001 |
| 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".