Большие глубины новые технологии
Bibliographic record
Abstract
Current state of mining industry is characterized by the trend towards rapid development of deep mining thus resulting in increased production costs of mineral extraction and having negative impact on safety of mining works. It is possible to decrease open-pit mining costs due to pit wall steeping following the fulfillment of a set of conditions whose neglecting can lead to serious consequences such as fatalities, machinery destruction, shutdown or decline in plant capacity. The calculation problem of pit wall and slope face optimum angle has become difficult due to required consideration of rock mass structure and its stress state as well as slope stability impacted by seasonal precipitation. This kind of problem has been solved for the Kovdorsky GOK. To provide this solution, the following measures were taken: geological-structural mapping of the deposit, pit wall design with double bench and vertical slope angles, development of smooth blasting, pit wall state and near contour rock mass monitoring system. Geomechanic state of the rock mass is of great importance for providing safety and stability of an underground mine’ work. Comprehensive solution of the safety problem is in transition to minimally-manned operation and to unmanned production techniques in the future. Accepting such technologies is future-proof not only in terms of miners’ safety but also in reduction of the costs related to comfortable and safe working conditions. Fully autonomous LHD machinery is already used in the mines of LKAB (Sweden) and Inca (Canada) as well as El Teniente (Chile) and Jundee (Australia). Over the last years technological process automation during open-pit mining is actively developed.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.188 | 0.107 |
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".