IDENTIFICATION AND ANALYSIS OF STRUCTURAL-TECTONIC FEATURES OF GEOLOGICAL TERRAINS USING LINEAMENT ANALYSIS: EXAMPLES OF GEOMODELLING FOR CANADIAN AND UKRAINIAN SHIELDS
Bibliographic record
Abstract
Nowadays, rare earth elements (REEs), which belong to the group of rare metals, are considered worldwide to be strategic critical raw materials and are extremely important for the economic development of any country. Various methods and approaches are used for prospecting and exploration of deposits of these critical metals; among which the methods of 3D geological modeling are currently prioritized, which allow a comprehensive analysis of the structural features of potentially promising areas as well as individual deposits. One of the methods used for REE exploration is structural mapping combined with geological terrain analysis, including structural lineament analysis. The latter is considered an important geological tool for identifying the primary and secondary structural and tectonic features of our study areas of investigation. The objectives of the present research work are: 1) to identify structural lineaments within two studied areas – the Alces Lake area (Northern Saskatchewan, Canadian Shield) and the Western Azov region (Azov block of the Ukrainian Shield) using automated and manual approaches, 2) to compare the results obtained for both areas, and 3) to discuss interpretation/conclusions over the overall suitability of the method for the exploration purposes. In the current research, we conducted the extraction and geospatial analysis of linear features and their tectonic interpretation. During the modeling process, remote sensing and geostatistical methods were used to analyze topographic, geological and geophysical data. As a result, the main structural lineament trends for the two studied areas were identified and structural-tectonic criteria for the formation and localization of deposits of rare earth elements were determined/proposed.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".