Integration of Stream Sediment Geochemical and Aster Data for Porphyry Copper Deposit Exploration in Khatun Abad, North West of Iran / Integracja geochemicznych danych o osadach dennych oraz danych pozyskanych z systemu aster do poszukiwań geologicznych w rejonie złóż miedzi porfirytowej w khatun abad, w północno-zachodniej części iranu
Bibliographic record
Abstract
Urumieh-Dokhtar magmatic belt is the host of large porphyry copper deposits in Iran. Khatun Abad area is located in north west part of this belt, so in this study, the stream sediment geochemical survey and hydrothermal alteration zones extracted from ASTER data were used to generation new target for future lithogeochemical survey. In this study after a brief discussion on descriptive statistics, principal component analysis (PCA) and hierarchical cluster analysis were used to compress the information to a few maps and to assist in determining multi-element associations. Then C-A fractal method was used for map classification. In order to extraction hydrothermal zones ASTER data were used. ASTER SWIR bands are most useful for the identification of alteration minerals such as Alunite, Pyrophylite, Kaolinite, Sericute and Carbonates. In this paper based on spectral analysis of ASTER SWIR data six maps of alteration zones were prepared. Geochemical study and spectral analysis of ASTER data showed that mineralization and alteration are limited to E1lb and gr lithological units and have NW-SE trends from east of Khatun Abad to Ghezeljeh-Gheshlaghi.
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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 | 0.002 | 0.002 |
| 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".