Data infrastructure for a tactical mine management system
Bibliographic record
Abstract
An evolution in tactical mine management systems is underway, powered by IT and new management tools. This paper considers the specific data infrastructure needs for such systems that were identified in field studies in operating underground metal mines. These were formulated into a methodology to create a mine-focused information system through structured data modelling and process mapping. The importance of data items particular to mining systems became evident through this study, such as workplace, process descriptions, and details of production process outputs. Some of these new data items can be used to integrate data thereby enabling new management tools and techniques. The application of the data infrastructure design methodology at operating mines was seen to improve management's understanding of the production system and to enable the creation of tactical management tools. This paper discusses the need, principles, and specifics of designing a data infrastructure specifically for underground metal mining. It stems from a collaborative PhD study that developed a methodology to create a tactical mine management system that was applied in underground mines in the Sudbury basin, in Ontario Canada.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".