New Mineral NamesGeschieberite and SvornostiteImayoshiitePalladosilicidePlášiliteRaisaiteShchurovskyite and dmisokoloviteVanackerite
Bibliographic record
Abstract
This New Mineral Names has entries for 9 new mineral species, including dmisokolovite, geschieberite, imayoshiite, palladosilicide, plasilite, raisaite, shchurovskyite, svornostite, and vanackerite. # Geschieberite* and Svornostite* {#article-title-2} J. Plasil, J. Hlousek, A.V. Kasatkin, R. Skoda, M. Novak and J. Cejka (2015) Geschieberite, K2(UO2)(SO4)2(H2O)2, a new uranyl sulfate mineral from Jachymov. Mineralogical Magazine, 79(1), 205–216. J. Plasil, J. Hlousek, A.V. Kasatkin, M. Novak, J. Cejka and L. Lapcak (2015) Svornostite, K2Mg[(UO2)(SO4)2]2·8H2O, a new uranyl sulfate mineral from Jachymov, Czech Republic. Journal of Geosciences, 60, 113–121. Two new uranyl sulfates, geschieberite (IMA 2014-006), ideally K2(UO2)(SO4)2(H2O)2 and svornostite (IMA 2014-078), ideally K2Mg[(UO2)(SO4)2]2·(H2O)8 were recently discovered in the Geschieber vein at the Svornost mine, Jachymov (Joachimsthal), Western Bohemia, Czech Republic, and named for their type locality. The Jachymov ore district is a classic example of the Variscan hydrothermal vein type of deposit, so-called five-element formation, Ag–Bi–Co–Ni–U. Both new minerals are the supergene products of the post mining alteration of the uraninite and sulfides of the primary ore. They occur in the close association with each another and with adolfpateraite, gypsum and mathesiusite. Both minerals exhibit strong yellowish green fluorescence under both short- and long-wave UV radiation. Both are brittle, have an uneven fracture, and estimated Mohs hardness of ~2. …
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.032 | 0.007 |
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".