Assessment of mineral resources for Yukon’s gold mining district using GIS technologies
Bibliographic record
Abstract
Purpose. To develop a methodological approach to the assessment of mineral resources of the Yukon gold mining region using modern geoinformation technologies (GIS). The methodology used: 3D modeling of mineral deposits. Results. A visualization of a mineral map was created using a geoinformation model of the deposit. A cartographic analysis of the Yukon gold mining region was performed, including a detailed location of deposits and potential mining areas. A mineral resource distribution model was formed for the assessment of gold reserves using GIS technologies. Originality. The scientific novelty lies in the integration of multidimensional data and high-tech algorithms to create visually understandable models of mineral distribution. This contributes not only to more effective planning of mining operations, but also provides the opportunity to take into account environmental factors for more sustainable management of natural resources. In addition, the proposed methodology can be adapted for different types of minerals and geographical conditions, which makes it a universal tool in geological exploration and reserve assessment. This opens up new prospects for the use of GIS technologies in the mining industry. Practical value. Thanks to the use of multidimensional data analysis, it is possible to minimize the risks of errors and optimize the mining process, reducing the costs of drilling and exploration. In addition, the detailing of underground structures allows you to take into account environmental factors, choosing mining areas with minimal impact on nature. The versatility of the method ensures the possibility of its adaptation to the extraction of various minerals and conditions, which makes it an important tool for the development of the modern mining industry. The implementation of this approach will contribute to sustainable management of natural resources and increasing the environmental responsibility of the industry.
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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 | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".