A cluster-based multiparametric similarity test for the compartmentalization of crystalline rocks into structural domains
Bibliographic record
Abstract
Usually, fracture sampling studies comprise the collection of several fracture samples that involve many fracture clusters. Grouping fracture samples into structural domains is generally useful for geologists, hydrogeologists and geomechanicians as a region of fractured rock is subdivided into subregions with similar behaviour in terms of their hydromechanical properties. One of the common methods used for grouping fracture samples into structural domains considers the fracture orientation of clusters and ignores several fracture parameters, such as fracture spacing, aperture and persistence, which are important for fluid circulation in the rock mass. In this study, we proposed a new cluster-based similarity method that considers the orientation of clusters as well as the clusters’ aperture, persistence and fracture spacing. Field investigations were conducted in the Grenville geological province of the Canadian Shield in the Lanaudière region, Quebec, Canada, where fractures were sampled from 30 outcrops and four boreholes. The proposed method is more suitable than other methods, and has applications in hydrogeology, rock mechanics and particularly in studies of fluid circulation in the rock mass. In addition, a method for the compartmentalization of a given study area into structural domains by means of Voronoi diagrams is also proposed.
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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 | 0.009 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".