Determining the required underground grout pack production profile for narrow tabular mining operations
Bibliographic record
Abstract
South African gold and platinum mines commonly utilise narrow tabular mining for ore extraction. The removal of the ore body leaves open voids with stoping widths that can exceed 2 m in some mines. Props and cementitious grout packs are used extensively as stope support systems to stabilize the rock mass of the voids and to reduce the risks associated with rockfalls and rockbursts. The construction of grout packs for support follows the stope face advance and mining production is inter alia dependant on the production profile of the grout packs. The grout pack production system of a mine generally consists of a surface mixing plant and a reticulation system to the underground workings. A cementitious grout slurry is prepared at the mixing plant and is pumped to the underground stopes for filling of grout packs positioned between the hanging wall and footwall. Construction of grout packs in stopes is labour intensive and requires planning to ensure that the grout pack production profile meets the mining production profile. To determine the planned monthly grout volumes of a mining operation a variety of parameters need to be considered. These parameters should be accurately estimated at the start of a production month and are based inter alia on historical data and the mining plan of the operation. This paper presents a method to assist mining staff in estimating the required grout pack production profile, determining the required capacity of grout plants and reconciliation of the actual and planned grout pack production profile for a production month.
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 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".