Fractal analysis of veins in <scp>P</scp>ermian carbonate rocks in the <scp>L</scp>ingtanchang anticline, western <scp>C</scp>hina
Bibliographic record
Abstract
Abstract Statistical analysis of the thickness distribution of veins in the Lingtanchang structure, southern Sichuan basin, western China, indicates that vein thickness conforms to fractal character and a power‐law distribution, whereas various distributions of veins are indicated by both the spacing distribution and the Cv values. According to geometry and structure, the veins in the Lingtanchang structure can be divided into confined and through‐going veins. The statistical analyses show that confined intralayer veins are consistent with a power‐law distribution in thickness, with Dt values of 1.1–1.7 and a log‐normal distribution in spacing with Cv values of 0.8–0.9. The confined intra‐ to interlayer veins show Dt values of 1.0–1.3 and an exponential distribution in spacing, with Cv values of 0.9–1.0, indicating an unconnected vein network with weak ability for paleofluid flow. However, the through‐going veins show the lowest Dt values of 0.6–0.8 with a power‐law distribution in thickness and power‐law to exponential distribution in spacing with Cv values of 1.5–3.2. Differences in spacing distribution and in the thickness of veins can be explained by different stages of vein growth from confined to through‐going veins. Such processes are dominant with percolating cluster models, which significantly controls spatial distribution of veins and paleofluid flow, and therefore the reservoir conditions in the southern Sichuan basin.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| 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".