Recommendations for the minimum number of laboratory tests for intact rock
Bibliographic record
Abstract
The design of mining excavations in rock requires access to a representative geotechnical model that includes the mechanical properties of the rock mass. The available geotechnical data provide the necessary input to analytical, numerical, and empirical design tools. Consequently, any geotechnical analysis is influenced by the quality of the input data. Therefore, to understand and mitigate the design risk caused by data uncertainty, it is critical to evaluate the level of confidence in the collected geotechnical data. A practical limitation of current mine design practice is the absence of quantitative guidelines to select the number of laboratory tests required. This investigation employs small-sampling theory to determine the minimum number of tests necessary to obtain predefined confidence intervals in intact rock estimates at South African mines. A key element of this work is the introduction of geotechnical domain complexity as a significant factor in establishing quantitative recommendations for the required minimum number of laboratory tests. A tangible contribution of this work is the development of an original methodology for planning laboratory testing campaigns for a new mining project or for updating the geotechnical database of operating mines. The proposed quantitative methods can eventually replace subjective assessments in addressing data collection requirements.
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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 | 0.066 | 0.208 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.010 | 0.003 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.008 |
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".