Adapting methodologies from the forestry industry to measure the productivity of underground hard rock mining equipment
Bibliographic record
Abstract
The purpose of this dissertation is to develop and apply a framework to characterize the ground support installation component of the mining development cycle in underground hard rock mines for the purposes of comparing equipment. A secondary goal is to identify opportunities to improve the productivity of the ground support installation process. It was found that the forestry industry faces similar challenges as the mining industry when measuring equipment output in a variable environment where equipment productivity is affected by a range of external conditions. Despite this challenge, forestry researchers successfully developed and applied a standardized methodology and nomenclature to measure the productivity of equipment for the purposes of equipment and process comparison in variable external conditions. The methodology used in the forestry industry was modified to measure mechanized and semimechanized ground support installation productivity in three Canadian underground hard rock mines. Furthermore, opportunities to improve the ground support installation process were identified. This framework can be modified to measure and compare other types of mining equipment. By using a standardized methodology to measure, compare and improve mining processes, development and production rates can be increased in underground hard rock mines. In summary, a framework was adapted from the forestry industry to measure and compare the productivity of the ground support installation cycle in three Canadian hard rock mines, and opportunities to improve the process were found.
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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 | 0.012 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.015 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".