Quantifying the influence of geotechnical borehole inclination on collecting joint orientation data
Bibliographic record
Abstract
A comprehensive geotechnical drilling program is necessary to ensure a sufficient degree of confidence in constructing a structural model. This paper investigates the influence of the borehole inclination on the characterisation of the joint orientation. Discrete Fracture Networks (DFNs) are used to model joint set populations. The average joint properties are obtained by simulating boreholes in the DFN for three scenarios: vertical holes vs. inclined holes, increasing proportion of inclined holes and variable borehole inclinations. The results show that the estimated average dip direction is not significantly influenced by the borehole inclination, but the average dip value is closer to the population mean value for inclined holes. The percentage of joints intercepted increases when more than 70% of the boreholes are inclined. A higher percentage of joints is intercepted, and a better estimate of the average joint set dip is obtained when using boreholes at an inclination between 58 and 63 degrees.
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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 | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".