State-of-the art and future considerations on drilling-and-blasting system at plants of Metalloinvest
Bibliographic record
Abstract
The ore supply base of Metalloinvest company is composed of products of two structural divisions, namely, Russia’s largest Lebedinsky and Mikhailovsky mining and processing plants (GOKs). The buildup of the strategic competitive power is considered by Metalloinvest management in the form of reaching high economic effi ciency of all production processes by means of improvement of the existing and introduction of new technologies. Among the starting and key processes is drilling-andblasting, reliability and efficiency of which govern the failure-free and smooth performance of the open pit mining and processing systems. In 1996 Lebedinsky GOK became one of Russian pioneer producers of emulsion explosives of the type of Tovan based on the technology of ETI, Canada, and in 2001 Mikhailovsky GOK was the fi rst to implement a modular technology developed by GosNII Kristal for the production of Granemits. Specialized drilling-and-blasting services allow solving scale-wise unique production problems, the annual quantity of blasted rock makes 70 Mm3 in high-strength rock mass under complicated ground and hydrogeological conditions. Another difficulty is represented by the accepted four-week cycle of production blasting. Aimed to cut down the fi nal cost of products, the production and investment policy of Metalloinvest has enabled large-scale technical upgrading of drilling rigs and charging machines as well as rapid modernization of production of emulsion explosives. As a result, inside the last three years, blasting expenditures have been reduced by more than 20%.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.005 |
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".