Quantitative characterization of fracture surface undulations and gas-guiding patterns in fractured rocks under steady loading
Bibliographic record
Abstract
Fractures in rock strata serve as flow pathways for gas flow. The undulation of fracture channels can influence the guidance of gas flow. In this context, four-point bending experiments on prefabricated fractured rocks at different angles under stable stepped loading stress. The experiment results clarified the evolutionary law that the undulation degree of the rock tensile fracture surface is separated by an initial fracture angle of 45°. The high undulation intervals were less than 45°, whereas the low undulation intervals were more than 45°. Furthermore, the relative undulation degree, undulation frequency, and matching degree of the fracture surface were quantified. The relationship between the change in fracture surface undulation and gas flow guidance was established. Based on this, the stability, tortuosity, and uniformity of the gas flow in the fracture channel were quantitatively characterized. Subsequently, numerical models of the fracture channels were constructed to validate the indices proposed in this study. The results of the study clarified the influence of different initial fracture angles on the undulation changes of fracture surfaces, and established the relationship between these changes and gas flow, which is conducive to understanding the role of internal fracture channels in rocks in guiding the gas flow process.
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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) | 0.000 | 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".