Relationships between Geology, Ore-body Genesis, and Rock Mass Characteristics in Block Caving Mines
Bibliographic record
Abstract
As Block Caving Mining becomes a more widely used method of extracting ore from deep, low-grade, weak, disseminated ore-bodies, it becomes important to understand the negative side effects associated with this mining method. One of the greatest side effects of concern is subsidence that results from the removal of material at depth. In order to better understand the processes affecting subsidence it is vital to first get an idea of the geological conditions that influence these subsidence inducing processes. Therefore it is the aim of this paper to shed some light on the various geological environments in which block caving is used to help single out the most important geological features, with respect to subsidence, that exist within each specific mining environment. Examples of important geological features would be ore-body dimensions and depth, the nature of the site specific rock units (ie. sedimentary or volcanic rocks of varying strengths), and the overall structural regime of the host geological environment (folding, faulting, shearing). Relationships that exist between geological characteristics at various mines will also be explored in the hope of finding similarities that can be used to link different mines and their corresponding subsidence responses. This will prove beneficial when attempting to find explanations as to why subsidence occurs in the manner that it does at a specific mine.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".