Bibliographic record
Abstract
Abstract In this paper, a c m model is proposed to quantify structural states of soft mudrocks, which are dependent on clay fractions and porosities. Physical properties of natural and reconstituted soft mudrock samples are used to derive two parameters in the c m model. With the c m model, a simplified homogenization approach is proposed to estimate geomechanical properties and fabric orientation distributions of soft mudrocks based on the mixture theory. Soft mudrocks are treated as a mixture of nonclay minerals and clay‐water composites. Nonclay minerals have a high stiffness and serve as a structural framework of mudrocks when they have a high volume fraction. Clay‐water composites occupy the void space among nonclay minerals and serve as an in‐fill matrix. With the increase of volume fraction of clay‐water composites, there is a transition in the structural state from the state of framework supported to the state of matrix supported. The decreases in shear strength and pore size as well as increases in compressibility and anisotropy in fabric are quantitatively related to such transition. The new homogenization approach based on the proposed c m model yields better performance evaluation than common effective medium modeling approaches because the interactions among nonclay minerals and clay‐water composites are considered. With wireline logging data, the c m model is applied to quantify the structural states of Colorado shale formations at different depths in the Cold Lake area, Alberta, Canada. Key geomechancial parameters are estimated based on the proposed homogenization approach and the critical intervals with low strength shale formations are identified.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.001 |
| 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 teacher head, 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".