Experience of introduction of modern computer programs in education and mining
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 it