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Coupling forward modelling of garnet growth with monazite geochronology: an application to the Rappold Complex (Austroalpine crystalline basement)

2008· article· en· W1976187498 on OpenAlexaff
Fred Gaidies, Erwin Krenn, Christian de Capitani, Rainer Abart

Bibliographic record

VenueJournal of Metamorphic Geology · 2008
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeological and Geochemical Analysis
Canadian institutionsUniversity of Calgary
FundersAustrian Science Fund
KeywordsMonaziteMetamorphismGeologyGeochemistryMetamorphic rockGeochronologyOrogenySillimaniteKyaniteZirconBiotitePaleontologyTectonics

Abstract

fetched live from OpenAlex

Abstract Results from forward modelling of garnet growth and U–Th–Pb chemical dating suggest three periods of metamorphism that affected metapelitic rocks of the Rappold Complex (Eastern European Alps). Garnet first grew during Barrovian‐type metamorphism, possibly during the Carboniferous Variscan orogeny. The second period of metamorphism produced monazite and resulted in minor garnet growth in some samples. Variable garnet growth was controlled by changes to the effective bulk rock composition resulting from resorption of older garnet porphyroblasts. Monazite crystals have variable morphology, textures and composition, but all yield Permian ages (267 ± 12 to 274 ± 17 Ma). In samples in which there was Permian garnet growth, monazite forms isolated and randomly distributed grains. In other samples, monazite formed pseudomorphous clusters after allanite. This difference is attributed to higher transport rates of monazite‐forming elements in samples which underwent dehydration reactions during renewed garnet growth. The third and final period of garnet growth took place during Eo‐Alpine (Cretaceous) metamorphism. Garnet of this age displays a wart‐like texture. This may reflect transport‐limited growth, possibly as a result of repeated dehydration during polyphase metamorphism.

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)0.001
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.130
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
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 head0.039
GPT teacher head0.209
Teacher spread0.171 · 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 designSimulation or modeling
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

Citations42
Published2008
Admission routes1
Has abstractyes

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