Bibliographic record
Abstract
This thesis investigates face time optimisation at TauTona underground gold mine in South Africa. TauTona is located 70 km south-west of Johannesburg. Face time optimisation involves decreasing the time taken for workers to get to and from the workplace and increasing the productivity of workers at the workplace. This thesis shows that face time optimisation has a significant role to play at TauTona and includes recommendations to improve face time.TauTona is the deepest operating mine in the world with a shaft depth of 3.8 km. TauTona predominantly uses longwall mining and employs 5,600 persons. The need for face time optimisation is due to difficulties getting workers to and from the workplace and the harsh in-stope environment. Workers travel through one to three shafts and horizontal distances to 6 km by mancarriage or foot to the waiting place. They then proceed by foot to the workplace. The average stope height is 0.9 m with a target wet-bulb temperature of 28.5°C.In 2nd quarter 2002 the average round trip to and from the workplace was 4 hours and 6 minutes. In 2nd quarter of 2003 the average time get to the workplace was 3 hours and 15 minutes. This improvement has been despite shift durations decreasing. While this suggests that the face time optimisation program at TauTona is succeeding, the high standard deviation in the face time achieved means there is room for improvement.The largest component of travel time is walking. The greatest delay in getting to the workplace is waiting for mancarriages, with mancarriage availability on the way out poorer than on the way in. Mancarriages need have a greater availability, mancarriage drop-off points need to closer to the waiting places, waiting places need to be closer to the workplace. Travellingway and gully conditions need to be improved. Waiting times for the cages are gradually increasing in the main and sub-vertical shaft, and dramatically increasing in the tertiary-vertical shaft. The shaft timetable needs to be re-evaluated, possibly with the help of mathematical modelling or simulation software. All these areas need to be monitored on an ongoing basis....
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".