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Record W2391650333

SIMULATION AND ANALYSIS OF NONLINEAR AND SELF-ORGANIZING GROWTH COURSE IN PLAGIOCLASE ZONING THROUGH CELLULAR AUTOMATA

2011· article· en· W2391650333 on OpenAlexaff
C QIUMING

Bibliographic record

VenueJournal of Mineralogy and Petrology · 2011
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicGeochemistry and Geochronology of Asian Mineral Deposits
Canadian institutionsYork University
Fundersnot available
KeywordsPlagioclaseZoningCellular automatonMagmaGeologyNonlinear systemComputer scienceIntrusionGeochemistryEngineeringPhysicsCivil engineeringArtificial intelligenceVolcano
DOInot available

Abstract

fetched live from OpenAlex

The purpose of this paper is to discuss the kinetic mechanism of zoning in plagioclase through simulation based on some models.In the period of 1980 s to 1990 s,some Chinese scholars started studying on zoning growth in plagioclase in the perspective of nonlinear science and self-organization,which was initiated by some foreign scholars.Recent years,some scholars consider that the formation of oscillatory zoning occurs only when plagioclase grows up under the condition of later magma intrusions.In this paper,a new research approach of Cellular-Automata analysis tool in the platform of Matlab as the analytic method of nonlinear and self-organization is introduced.Based on Fick′s diffusion law and Cellular-Automata analysis,this study simulates several different magmatic settings to understand zoning growth courses.It is indicated that the formation of oscillatory zoning not only relates to the magmatic setting of later magma intrusion,but also to the resaturation of concentration of CaAl2Si2O8 in the magmatic liquid around the crystalline plagioclase.

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 score0.065
Threshold uncertainty score0.480

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.021
GPT teacher head0.221
Teacher spread0.201 · 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

Citations0
Published2011
Admission routes1
Has abstractyes

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