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Record W2051237515 · doi:10.2113/gscanmin.38.3.707

THE CONTRASTING RESPONSES OF MUSCOVITE AND PARAGONITE TO INCREASING PRESSURE: PETROLOGICAL IMPLICATIONS

2000· article· en· W2051237515 on OpenAlexvenueno aff
C. V. Guidotti, Francesco Sassi, Paola Comodi, P. F. Zanazzi, James G. Blencoe

Bibliographic record

VenueThe Canadian Mineralogist · 2000
Typearticle
Languageen
FieldPhysics and Astronomy
TopicPlanetary Science and Exploration
Canadian institutionsnot available
FundersOak Ridge National LaboratoryOffice of Energy EfficiencyGeothermal Technologies ProgramOffice of Energy Efficiency and Renewable EnergyU.S. Department of Energy
KeywordsMuscoviteSubstitution (logic)GeologyContext (archaeology)MineralogyMineralChemistryCrystallography

Abstract

fetched live from OpenAlex

The incorporation of Fe, Mg, and Si into muscovite in response to increase of pressure (P) has long been recognized. In the context of the appropriate mineral assemblages, the extent of this substitution has been calibrated to serve as a very useful geobarometer for high-P parageneses. In marked contrast, little or no Fm i.e., S(Mg + Fetotal), substitutes into paragonite regard-less of P. To date, Fm substitution into muscovite has been considered only in terms of DVr with little consideration of the crystallochemical aspects of this substitution. Moreover, the substitution seems to occur in response to simple exchange-reactions involving little or no dehydration. Such reactions typically have a negligible DVr. Drawing upon the implications of studies combining high-P refinement of the crystal structures and measurement of compressibility, we suggest that high P causes struc-tural changes in low-Fm muscovite that destabilize it. However, implementation of the Fm substitution facilitates structural adjustments, which reduce this instability. In contrast, paragonite is not only intrinsically less compressible than muscovite, but any substantial amount of Fm substitution would destabilize it.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.226
Threshold uncertainty score0.897

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.021
GPT teacher head0.242
Teacher spread0.221 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations15
Published2000
Admission routes1
Has abstractyes

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