Quantifying influence of drilling additional boreholes on quality of geological model
Bibliographic record
Abstract
Geotechnical stability analysis in open-pit mines requires access to a representative geotechnical model. The confidence level in the collected geotechnical data influences slope design. This paper investigates the influence of the number of boreholes, drilled to collect geological information, on the quality of one component of the geotechnical model, the geological model. The number of boreholes influences the number of rock core samples collected for the identification of rock type, and the definition of geotechnical domains and their boundaries within the rock mass. A challenge in the definition of the geotechnical domains is the determination of the drill hole density that minimizes the variation in the interpreted geological model from the actual rock mass. To quantify the influence of the drill hole density, boreholes are simulated in the most recently updated geological model for three mine sites. The simulated drill hole density is increased progressively until the variation of the interpreted section, compared with the original section, is minimized. A classification strategy was developed to determine the complexity level for each geotechnical domain. Furthermore, a series of empirical quantitative guidelines are presented prescribing the minimum drill hole density per domain complexity, while limiting variations from the actual rock mass.
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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 | 0.007 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".