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Record W1622237323 · doi:10.1029/2009gc002828

Micromagnetics of paleomagnetically significant mineral grains with complex morphology

2010· article· en· W1622237323 on OpenAlexafffund
Wyn Williams, Michael Evans, David Krása

Bibliographic record

VenueGeochemistry Geophysics Geosystems · 2010
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGeomagnetism and Paleomagnetism Studies
Canadian institutionsUniversity of Alberta
FundersEngineering and Physical Sciences Research CouncilNatural Sciences and Engineering Research Council of CanadaNatural Environment Research CouncilSight Research UKLeverhulme Trust
KeywordsMicromagneticsMagnetiteCoercivitySPHERESGeologyGrain boundaryCrystalliteMaterials scienceOctahedronCondensed matter physicsGeometryMineralogyMagnetizationCrystal structureCrystallographyComposite materialPhysicsMagnetic fieldMicrostructureMathematicsChemistryMetallurgy

Abstract

fetched live from OpenAlex

Micromagnetic calculations relevant to paleomagnetism have generally focused on ideal shapes such as spheres and cubes. However, oxide grains that occur naturally in rocks often have irregular morphologies, highly skeletal dendritic forms being particularly common. To investigate the potential effects of complex grain morphology on magnetic stability, we have carried out two parallel sets of calculations. The first is based on randomly distorted 30–120 nm magnetite spheres. We use a three‐dimensional finite element/boundary integral micromagnetic model able to generate suitable morphologies unrestricted by the regular cell structure required by finite difference models. The second model consists of a 300 nm magnetite octahedron from which most of the material has been removed to leave a small octahedral crystallite at each of the six apices connected by an orthogonal framework of thin rods. The results obtained imply that micromagnetic models currently provide no unambiguous evidence that morphological complexities endow magnetite nanoparticles with enhanced coercivity.

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 categoriesMeta-epidemiology (narrow)
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 score0.188
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.007
GPT teacher head0.207
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.

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

Citations22
Published2010
Admission routes2
Has abstractyes

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