Stistaites of the South Urals
Bibliographic record
Abstract
Research subject. Rare minerals of tin and antimony – stistaites from natural lead plates from the Severo-Svetlinskaya placer in the Chelyabinsk region and from microspherules of intermetallic compounds in the products of erosion of granites of the Kisegach complex in the Ilmeny Mountains. Materials and methods. Electron probe analysis and laser ablation with inductively coupled plasma were used to study the composition of the predominant minerals of intermetallic compounds in lead plates extracted during the washing of a gold-bearing placer, as well as from metal microspherules in the sandy fraction of eroded granites. Results. Two types of stistaite were identified: lead and arsenic-lead. Lead stistaites is sharply predominant, with its average composition (wt %) being Sb – 47.39, Sn – 38.75, Pb – 13.24, Cu – 0.06. The average composition of arsenic-lead stystaite (wt %) was found to be Sb – 43.89, Sn – 41.06, Pb – 11.02, As – 3.05, Cu – 0.47. Tin-lead microspherules from the destruction products of biotite granites of the Kisegach massif (Ilmeny Mountains) occasionally contain crystals and spotted precipitates of lead stistaite with the composition (wt %) of Sn 53.54, Sb 38.45, and Pb 7.42. Conclusions. It is assumed that, in both cases, the formation of alloys of intermetallic compounds of tin, lead and antimony with inclusions of native copper and iron was associated with granite magmatism.
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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 | 0.001 | 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) | 0.002 | 0.001 |
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".