Structural and alteration zones controls on Cu mineralisation in the northwest of Nain (northeastern Isfahan, Iran): A remote sensing perspective
Bibliographic record
Abstract
Remote sensing data can be utilised for regional mapping of the Earth's surface to enhance structural interpretation and mineral prospecting. To this end, satellite multispectral sensors such as the Advanced Space borne Thermal Emission and Reflectance (ASTER) with six channels in the shortwave infrared and five channels in the thermal area is helpful in detecting alteration and mineralisation zones in areas with good rock exposures. This study has investigated and detected hydrothermal alteration zones and mapped structural elements associated with mafic volcanic rocks-related copper mineralisation in the northwest of the Nain district in Central Iran. In this study, we processed ASTER data (14 bands). We generated maps that depict the distribution of alteration minerals (e.g., sericite, kaolinite , chlorite, and calcite) related to copper mineralisation using various techniques such as different band ratio images, False-colour composition (RGB), Matched Filtering (MF), and Spectral Angle Mapper (SAM). Follow-up ground proofing validated the analysis of results from the ASTER data. The study established the regional distribution of hydrothermal alteration zones (i.e., phyllic, argillic, and propylitic). The regional distribution and extent of these alteration zones are associated with regional structures that served as focusing conduits for the buoyant hypogene mineralizing fluids. The results show that ASTER imagery is useful in mapping the extent of the hydrothermal alteration and lithological units and can thus be used to target hydrothermal ore deposits with large alteration footprints.
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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 | 0.001 | 0.001 |
| 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 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".