INDIGENOUS MINERAL DEPOSITS IN THE TABLE D. I. MENDELEEV: WORLD DIMENSION
Bibliographic record
Abstract
Geography of mineral deposits and the distribution of chemical elements on the globe are characterized by heterogeneity. Mineral resources of the world, mineral deposits are devoted to a large array of publications of domestic and foreign specialists – geologists, geographers, geochemists, economists. During the mastering of the material, comparative-geographical, cartographic (analysis of maps of mineral resources, mineral resources in the context of continents and regions of the world), monographic (fundamental works of leading domestic and foreign geologists and resource scientists, geological and mineral reference books and dictionaries, multi-volume editions, devoted to the geology and mineral resources of individual countries and regions of the world) methods, systematic approach, in the processing and systematization of data used modern no computer technology. The explored deposits of mineral raw materials (actual and potential) form on the planet as separate local deposits, as well as geochemical zones – areas where concentrated economically valuable chemical elements and their compounds (minerals and rocks) are diverse in genesis (origin), stocks, exploitation possibilities. The largest of them are Appalachians in the USA – Western Hemisphere, High Velt in South Africa, Hibiny and Ural in Russia – Eastern Hemisphere. Leading countries in the territory where most of the geochemical raw materials are mined from the bowels are the USA (65 % of the total number of elements of the table), Russia (48 %), China (38 %), Canada (38 %), South Africa (30 %), Australia (27 %), Kazakhstan (19 %), India (14 %), Mexico (13 %). Systematized representations about the level of provision of mineral raw materials and minerals of individual countries and territories of the world. D. I. Mendeleev’s table and its mineral raw materials are presented as an objective factor of the international geographical division of labour. The given data reveal an adequate level of provision of countries and territories with mineral resources. The highlighted problem has confirmed the high density of interdisciplinary connections (geography, geology, geochemistry, economics, regionalisms). The given data can be implemented in the latest programs of reformed education in Ukraine.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".