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 \nsupport installation component of the mining development cycle in underground hard rock mines \nfor the purposes of comparing equipment. A secondary goal is to identify opportunities to \nimprove the productivity of the ground support installation process. \nIt was found that the forestry industry faces similar challenges as the mining industry when \nmeasuring equipment output in a variable environment where equipment productivity is affected \nby a range of external conditions. Despite this challenge, forestry researchers successfully \ndeveloped and applied a standardized methodology and nomenclature to measure the \nproductivity of equipment for the purposes of equipment and process comparison in variable \nexternal conditions. The methodology used in the forestry industry was modified to measure mechanized and semimechanized \nground support installation productivity in three Canadian underground hard rock \nmines. Furthermore, opportunities to improve the ground support installation process were \nidentified. This framework can be modified to measure and compare other types of mining \nequipment. By using a standardized methodology to measure, compare and improve mining \nprocesses, development and production rates can be increased in underground hard rock mines. \nIn summary, a framework was adapted from the forestry industry to measure and compare the \nproductivity of the ground support installation cycle in three Canadian hard rock mines, and \nopportunities to improve the process were found.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".