Chapter 7: Structural Analysis of Drill Core for Mineral Exploration and Mining: Review and Workflow Toward Domain-Based 3-D Interpretation
Bibliographic record
Abstract
Abstract Structural data is vital for the understanding of the geometry and evolution of a deposit and feeds into geologic, structural, resource, and geotechnical models. Accurate models are critical for targeting, resource estimation, and geotechnical design and, if rapidly available, support real-time decisions on drilling and grade control. Structural drill core data add a high-resolution data set to traditional data from mapping or the structural interpretation of remote sensing and geophysical data and, therefore, add indispensable information to any integrated model. In this paper we propose standardized workflows for data collection, review technological advances and quality control processes accelerating structural data collection from both oriented drill core and televiewer techniques, and provide an overview of structures that may be observed in drill core and discuss their significance to record for the geometry of the deposit. Critical to the data collection process is an interpretative process that recognizes and identifies domain-based structures that ultimately are fundamental to developing 3-D structural models.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.007 |
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".