Experience of introduction of modern computer programs in education and mining
Bibliographic record
Abstract
Numerous computer programs in service in the mining industry are conventionally grouped into programs of general purpose, special purpose, production control and production registration. The Chair of Geodesy and Surveying at the Institute of Mining and Mining Technologies, Kyrgyz State Technical University, has 20 years long experience of successful introduction and application of the licensed popular state-of-the-art computer programs—leaders in the world’s mining practice: Gemcom and Micromine, compatible with AutoCad, etc. Theoretical and practical training on these programs is executed by senior staff of the Kyrgyz–Canadian Kumtor Gold Company. The lecturers of the Chair were trained and certified by the program manufacturer (or distributors). The courses on the programs are divided into two directions: mining and geological exploration. After the comprehensive studies into the programs, a student has skills in creation of data bases for reserves appraisal; optimization of key mine designs; operational calculation of production output per any period of report; accumulation and unification of surveying measurement data, etc. The Center for Computer Technologies at the Chair handles applied problems and trains mining practitioners. Having mastered these programs, a student or an engineer can readily run analogous routines. Aimed to solve extra application tasks on mining, the Chair uses the own programs widely used in education and in mines owing to availability and simplicity. The integrated application of all these software products in education is highly effective in terms of training of engineers and in research and production management in mines.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.031 | 0.012 |
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".