Gorbunovite CsLi<sub>2</sub>(Ti,Fe<sup>+3</sup>)(Si<sub>4</sub>O<sub>10</sub>)(F,OH,O)<sub>2</sub> – a new mica supergroup mineral from the Darai-Pioz alkaline massif, Tajikistan
Bibliographic record
Abstract
Abstract Gorbunovite, CsLi2(Ti,Fe+3)(Si4O10)(F,OH,O)2, a new caesium trioctahedral mica, was discovered in the Darai-Pioz alkaline massif, Tajikistan. The mineral is named after the Russian chemist, leader of the Tajik-Pamir Expedition, Academician Nikolai Petrovich Gorbunov (1892–1938). Gorbunovite occurs as lamellar grains or flakes from 2 μm to 0.2 mm in a quartz–pectolite aggregate and is associated with quartz, fluorite, pectolite, baratovite, aegirine, leucosphenite, neptunite, reedmergnerite, orlovite, sokolovaite, mendeleevite-(Ce), odigitriaite, pekovite, zeravshanite, kirchhoffite and garmite. The mineral is colourless, transparent, with vitreous lustre. Mohs hardness is 2½. Dmeas. is 3.28 (2), Dcalc is 3.302 g/cm3. Gorbunovite is optically biaxial (–), α = 1.609(2), β = 1.621(2), γ = 1.623(2), 2Vmeas. 30(5) and 2Vcalc. 44. Gorbunovite is monoclinic, space group C2/m, C2 or Cm (polytype 1M), a = 5.236(2), b = 9.054(4), c = 10.767(4) Å, β = 99.61(4)°, V = 503.3(6) Å3 and Z = 2. The strongest lines in the powder X-ray diffraction pattern [d, Å, (I)] are: 4.49 (25), 3.94 (20), 3.69 (46), 3.57 (23), 3.45(34), 2.991 (42), 2.608 (77), 2.581 (100), 2.240 (33), 2.188 (62), 2.020 (24), 1.722 (27) and 1.511 (23). Chemical composition (microprobe analysis; H2O, Li2O – SIMS) is: SiO2 47.44, TiO2 9.40, Al2O3 0.66, MgO 0.63, Fe2O3 3.64, ZnO 1.01, K2O 0.39, Cs2O 26.64, Li2O 5.83, H2O 0.89, F 4.48, –O=F 1.89, total 99.17. The empirical formula is (Cs0.96K0.04)Σ1.00Li1.98(Ti0.60Fe+30.23Mg0.08Al0.07Zn0.06)Σ1.04Si4.00O10(F1.19OH0.50O0.31)Σ2.
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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 | 0.000 |
| 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) | 0.002 | 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".