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Record W3108032007 · doi:10.31996/mru.2020.3.3-12

Geological exploration and industrial development of the Bilozerskyi iron ore region

2020· article· en· W3108032007 on OpenAlexaff
O. A. Lysenko, R. P. Kolotiievskyi, A. S. Stankin

Bibliographic record

VenueМінеральні ресурси України · 2020
Typearticle
Languageen
FieldEngineering
TopicMining and Gasification Technologies
Canadian institutionsIron Ore Company (Canada)
Fundersnot available
KeywordsGeologyIron oreMining engineeringEarth scienceGeochemistryArchaeologyGeography

Abstract

fetched live from OpenAlex

The latest information on geological and structural position and geological structure of deposits of rich iron ores of the Belozerskyi iron ore district is summarized, and the state of their industrial development is characterized. During the exploitation of the Pivdenno-Bilozirske and Pereverzivske deposits, due to geological study, mining exploration and additional exploration of deep horizons, specialized and feasibility study a significant amount of new geological data has been received. This data allowed to clarify the position, morphology and internal structure of ore bodies, qualitative, physical and mechanical properties of ores and enclosing rocks, hydrogeological, geological, engineering and mining conditions. The article presents the results of the analysis and interpretation of the latest geological information and actual data on the mine development of deposits – modern views on the geological structure of objects, the characteristics of the basic natural conditions and parameters that affect the efficiency of industrial mining of rich iron ores. The state of geological prospecting in the region is very uneven. The most studied are the Pivdenno-Bilozirska and Pereverzivska fields, which are currently being developed. The rich iron ores of these deposits are classified as high quality in both Ukraine and Europe. These ores do not require beneficiation and are suitable for agglomeration and open-hearth process. This circumstance helps to reduce the cost of mining and increase the commercial efficiency. For the mine development of both deposits, a common infrastructure is used – permanent mine openings, mine shafts, support facilities and services. Joint development of the deposits will ensure the long-term stable operation of the plant without reducing the production of rich iron ores. The deposits are characterized by difficult mining and technical operating conditions. The ore deposits are covered by a cover up to 250–350 meters of loose water-saturated sedimentary deposits. The iron ore strata and the host rocks have heterogeneous physical and mechanical properties. Iron ores are mined using a productive level-room system. Empty rooms are filled consolidating stowing. The advanced drainage and continuous groundwater withdrawal from mines are used in the mining process. The progressive system of iron ores mining ensures: preservation of the productivity and quality of aquifers, which are used for municipal water supply; preservation of the earth’s surface from avalanches and landslides in the mining area; stable environmental conditions at the plant area and in the surrounding area; efficiency and completeness of extraction of iron ores from the bowels; the optimal level of economic indicators of the enterprise.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.027
Threshold uncertainty score0.055

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.132
GPT teacher head0.218
Teacher spread0.086 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2020
Admission routes1
Has abstractyes

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