Environmentally sound technologies of ore drawing with waste rock disposal in underground mines
Bibliographic record
Abstract
Abstract The work deals with studies of broken ore drawing and transporting from blocks and subsequent disposal of waste rocks in the mined-out space of underground mines by means of self-propelled underground loaders (ULs) using loading faces. When loading broken ore, incomplete loading of the machine bucket when digging it in at an angle to the ore pile is proved to be the main disadvantage. This reduces efficiency of using self-propelled ULs while transporting broken ore. Additionally, when digging the UL bucket in at an angle to the pile, the load on one of the sides of the machine increases, which causes corresponding adverse transformations of the UL design. To eliminate the above disadvantages, the structure of the block bottom is designed and proposed with loading workings with the front-end loading of the ore when digging the UL bucket in at an angle of 90° to the pile. The proposed technologies are proved to enable an increase in the volume of the bucket filling per cycle. The increased amount of broken ore in the bucket contributes to increased productivity of the load-transport equipment, thus reducing the adverse impact of rock pressure on workings of the block bottom by reducing the time for mining the block. The work proposes resource-saving technologies that will allow underground mining of rich ores with subsequent disposal of waste rocks from workings in the mined-out space of operating underground mines applying self-propelled loaders. The work also studies issues of the impact of the technology of underground mining and disposal of mining and metallurgical wastes on stability of the rock massif and the daylight surface. The proposed highly efficient technologies enable stabilizing geodynamic processes in the rock massif through backfilling the mined-out space.
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 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 | 0.000 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".