New data about noble-metal mineralization of Kingashsky ultramafic massif (northwest of Eastern Sayan)
Bibliographic record
Abstract
The relevance of the work is due to the need of detailed petrological studies of numerous potentially mineralized ultramafic and mafic-ultramafic massifs of the Kan block of the Eastern Sayan to improve the correlation of regional schemes, and to identify Pt-Cu-Ni mineralization in them. One of these massifs - Kingashsky massif, including an eponymous large Pt-Cu-Ni deposit, discovered in Soviet times - is the subject of this study. However, despite the increased interest of researchers to this massif, the following issues - the depth of its formation, the comagmatic ultramafic and mafic rocks and the conditions of formation and localization of ore in it - remain unresolved. The main aim of the paper: study of noble-metal mineralization in cumulative dunite of Kingashsky ultramafic massif in order to increase its mineralogical specialization. The methods used in the work: study of ore mineralization in polished sections using a polarizing microscope AxioScope Carl Zeiss, determination of the chemical composition of ore mineralization was carried out by the method of X-ray spectrum microanalysis using scanning electron microscope Tescan Vega II LMU with energy-dispersive and wave-dispersive spectrometers and microprobe Samebax-micro. The results. For the first time the authors identified and described new for this massif species of ore minerals of gold, silver and PGE: argentite, glandular sperrylite, Bi-bearing merenskyite. In general, the composition of noble-metal mineralization has an array of features due to the geochemical specialization of ore-magmatic system, which is characterized by a high iron content, which brings Kingashsky ore field together with other copper-nickel deposits of the Early Proterozoic: Dzhinchuan (China), Pechenga (Russia), Ungava (Canada), Mount Scholl (Western Australia) and others.
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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 | 0.001 |
| 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) | 0.001 | 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".