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Record W3015803786 · doi:10.3749/canmin.1900089

From structure topology to chemical composition. XXVIII. Titanium silicates: Jinshajiangite from the Oktyabr'skii Massif, Donetsk Region, Ukraine, a new occurrence

2020· article· en· W3015803786 on OpenAlexaffvenue
Maxwell C. Day, Elena Sokolova, F. C. Hawthorne, Robert T. Downs

Bibliographic record

VenueThe Canadian Mineralogist · 2020
Typearticle
Languageen
FieldMaterials Science
TopicCrystal Structures and Properties
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsElectron microprobeMassifChemical compositionCrystallographyAnalytical Chemistry (journal)TitaniumStructural formulaMineralogyMaterials scienceGeologyChemistryPhysicsGeochemistryMetallurgyThermodynamics

Abstract

fetched live from OpenAlex

ABSTRACT Here we report electron-microprobe data and unit-cell parameters for jinshajiangite, ideally NaBaFe2+4Ti2(Si2O7)2O2(OH)2F, from a new locality: the Oktyabr'skii massif in the coastal area of the Azov Sea, Donetsk region, Ukraine. Chemical analysis by electron microprobe gave Nb2O5 1.59, ZrO2 0.61, TiO2 17.07, SiO2 27.60, Al2O3 0.08, Fe2O3 2.04, FeO 16.42, BaO 9.81, ZnO 0.76, MnO 12.97, CaO 1.82, MgO 0.07, K2O 2.05, Na2O 2.51, F 2.48, H2O 1.92, O = F –1.04, sum 98.76 wt.%; H2O was determined in accord with the required number of monovalent anions for the Ti-dominant perraultite-type minerals: OH + F = 3 pfu; the Fe3+/Fe2+ ratio was assigned in accord with Mössbauer-spectroscopy results for jinshajiangite from a different locality. The empirical formula calculated on the basis of 19 (O + F) is (Na0.71Ca0.28□0.01)Σ1(Ba0.56K0.38□0.06)Σ1(Fe2+1.99Mn1.59Fe3+0.22Zn0.08Mg0.02Al0.01□0.09)Σ4 (Ti1.86Nb0.10Zr0.04)Σ2(Si4.00O14)O2[(OH)1.86F0.14]Σ2F1.00, Z = 4. Unit-cell parameters from the single-crystal data were determined by least-squares refinement of 9807 reflections with I > 10σI and are as follows: a = 10.726(8), b = 13.834(10), c = 11.065(8) Å, α = 108.172(5), β = 99.251(7), γ = 90.00(1)°, V = 1537.5(3.4) Å3, space group C .

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.408
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.031
GPT teacher head0.227
Teacher spread0.196 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2020
Admission routes2
Has abstractyes

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