Prediction Performance of Permeability Models in Gas-Hydrate-Bearing Sands
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Summary Permeability variation in the presence of gas hydrates (GH) is a major unknown in modeling hydrate dissociation in gas-hydrate-bearing sediment. Reduction of permeability in porous media occurs as a result of decreased porosity because of hydrate formation within pore spaces. In the absence of reliable experimental data, theoretical and empirical models have been proposed to establish the relationship between gas-hydrate saturation and permeability. The effectiveness of a particular permeability model in fitting the measured data has largely been qualitative through graphical analysis. In contrast, this paper introduces a quantitative performance measure to evaluate the effectiveness of an individual model in predicting the measured permeability. Second, a hybrid approach based on the weighted combination of existing permeability models is proposed. Permeability measurements from experimental and field studies were used to assess the prediction performance of various permeability models and the proposed hybrid approach.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 it