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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".