The Application of Various Mineral Prospectivity Modeling in the Exploration of Orogenic Gold Deposit in Saqez-Sardasht Region, Northwest Iran
Bibliographic record
Abstract
Abstract The Saqez-Sardasht region (~2000) is located in the north Sanandaj-Sirjan Zone (SSZ), between longitudes of 46°00′67 00″ E to 46°30′00″ and latitudes of 36°00′00″ N to 36°30′00″, northwest Iran. The region was fully studied to recognize the promising areas for gold deposits using various methods of fuzzy fusion techniques. Accordingly, six evidential layers (i.e., lithological, tectonic, alteration, with Au, Sb, and W geochemical anomalies have been derived from three geo-data sets of geology, geochemistry, and remote sensing. A concentration–number (C–N) fractal method was used to determine the geochemical threshold values. The outcome was then combined, using the multiple indicator kriging (MIK) geochemical methods to improve the mineral potential mapping of gold deposits. In this study, four various fuzzy mineral prospectivity mapping (MPM) methods consisting of conventional VIKOR, modified VIKOR, multi-class index overlay, and Geo Fuzzy Inference System (GeoFIS) have been employed to detect the most promising areas in the Saqez-Sardasht region. The MPMs were numerically compared to each other based on the MPM efficiency index for the seven gold prospects in the study region. The Geo Fuzzy Inference System (GeoFIS) acquired 91.84% agreement, so it is selected as the superior technique to lead the prospect selection. With affiliation to the outcome of MPM maps, promising mineralized areas, controlled by shear zones, were located in the southwestern part of the Saqez region.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".