Structural and geomorphological regional studies of the Kryvyi Rih-Kremenchuk suture zone using remote data
Bibliographic record
Abstract
The economy of Ukraine is raw material and depends on the prices of raw materials on world markets. The five largest iron ore producing countries accounted for 86% of its world production in 2020. China, with 41% of world production, was in first place, Ukraine was in seventh place, ahead of Canada and the USA. In terms of raw iron ore reserves, our country is also in seventh place. The largest reserves of iron ore in Ukraine are concentrated in deposits of the the Kryvyi Rih-Kremenchuk zone. The article deals with a set of structural, geomorphological and aerospace geological studies to identify the relative neotectonic activity of the blocks of the Kryvyi Rih-Kremenchuk suture zone, within which the predictive structures promising for the search for ore minerals are identified. A fundamentally new geologic and tectonic model of the Kryvyi Rih-Kremenchuk suture zone has been built, which is confirmed by the analysis of geophysical fields, structural, geomorphological and aerospace data. Because of our studies, it is proposed to pay attention to the object highlighted by our research - the Zhovtorichenska syncline area within the Ternovska depression of the Kryvyi Rih-Kremenchuk zone.
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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 | 0.003 | 0.002 |
| 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) | 0.001 | 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".