Bibliographic record
Abstract
The design of open stope rib pillars has been done using many empirical methods, but none of the methods has been verified with a design survey. This thesis uses data collected in the "Integrated Mine Design Study" to develop an empirical rib pillar design method for open, stope mining. The method is called the "pillar stability graph". The design variables in the method are: the compressive strength of the intact pillar material, the average pillar load determined by numerical modelling, the pillar width and the pillar height. The graph has been refined with the use of more than 80 literature case histories of hard rock pillars in room and pillar mining. The pillar stability graph and the pillar data base are used to examine the applicability of empirical methods commonly used in open stope rib pillar design. The investigation found the pillar strength curves developed by Hoek and Brown (1980) may be useful under some conditions for the design of open stope rib pillars but formulas by Hedley (1972), Obert and Duvall (1967) and Bieniawski (1983) are not applicable. Guidelines, using the pillar stability graph method, are proposed for the design of permanent open stope rib pillars, stable temporary open stope rib pillars, and failing temporary open stope rib pillars.
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.
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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".