MétaCan
Menu
Back to cohort
Record W4399738140 · doi:10.3390/min14060611

Quantification of Feldspar and Quartz Nucleation Delay in a Hydrous Peraluminous Granitic Melt

2024· article· en· W4399738140 on OpenAlexaff
Maude Bilodeau, Don R. Baker

Bibliographic record

VenueMinerals · 2024
Typearticle
Languageen
FieldMaterials Science
TopicCalcium Carbonate Crystallization and Inhibition
Canadian institutionsMcGill University
Fundersnot available
KeywordsFelsicNucleationFeldsparCrystallizationQuartzMelt inclusionsMineralogyMaterials sciencePegmatiteAnalytical Chemistry (journal)GeologyThermodynamicsChemistryGeochemistryMaficComposite materialPhysics

Abstract

fetched live from OpenAlex

A modified model based on classical nucleation theory was applied to a natural hydrous peraluminous pegmatite composition and tested against crystallization experiments in order to further investigate the quantification of nucleation delay in felsic melts. Crystallization experiments were performed in a piston-cylinder apparatus at 630 MPa and temperatures between 650 and 1000 °C for durations ranging from 0.3 to 211 h. Experimental run products were investigated by scanning electron microscopy paired with energy dispersive spectroscopy analyses of both crystalline and quenched liquid phases, the results of which were compared to an established theoretical nucleation delay model from the literature. The experiments showed good agreement (within a factor of 5) with the model for quartz, while it showed moderate agreement (within a factor of 10) with the model for sodic feldspar. Other crystals also nucleated, demonstrating abundant features of disequilibrium. Our research further demonstrates the potential of the model to predict nucleation delay, showing promising results for the quantification of the nucleation delay of quartz and feldspar in natural felsic melts, thus adding to previously published studies on hydrous, metaluminous, felsic melts and dry basaltic melts.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.119
Threshold uncertainty score0.297

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 science0.0000.000
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 head0.018
GPT teacher head0.256
Teacher spread0.238 · 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 designBench or experimental
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

Citations3
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueMineralsSame topicCalcium Carbonate Crystallization and InhibitionFrench-language works237,207