Building stone resources of Dnipropetrovsk region
Bibliographic record
Abstract
Abstract The article deals with the analysis of building stone resources of Dnipropetrovsk region that are used and can be used in order to provide construction needs. Dnipropetrovsk region is one of the most economically developed Ukrainian regions due to mineral and raw material resources being located on its territory. A part of regional mineral raw extraction comes up to almost 50% of mineral deposit balance reserves and the provision exceeds three times the national rate. Crystalline Pre-Cambrian rocks of East European platform fundament as gneiss, granites, quartzites, migmatites, granodiorites, amphibolites and sedimentary apron rocks – malmrocks – are natural construction material in the region. 42 building stone deposits are located on the territory of the region among them 19 deposits are developed also refer to big and middle and 24 are not developed. The biggest amount of developed deposits is located in the Dnipro, Kryvyi Rih, Kamianske and Nikopol districts. Building stone extraction is equal to approximately 14% from national quantity. Deposit exploitation is performed by commercial structures and state corporation enterprises. The conclusions are made about the ways of expanding capacities of building stone extraction due to complex iron ore deposit development and the opportunity of building stone reserve increase in the region.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".