Decision making using the analytic hierarchy process in mining engineering
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Experience and intuition have traditionally been central to decision-making in mining because of the frequent lack of quantitative data. Qualitative analysis is based primarily on the judgement, knowledge and experience of one or more experts. In cases where limited information is available, then subjective probabilities, based on general professional experience, knowledge, and opinion of experts, can be the basis for analysis. A methodology for qualitative decision-making using the analytic hierarchy process (AHP) mathematics and sensitivity analyses is presented herein. This paper presents a series of case studies in different mining scenarios to demonstrate the application of AHP. These relate to: investment analysis of new technology; ground support design; tunnelling systems’ design; shaft location selection; and mine-planning risk assessment. A review is given of the AHP methodology for qualitative decision making based on field applications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it