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Record W2062440467 · doi:10.1144/gsl.sp.2003.213.01.04

Water diffusion in natural potassic melts

2003· article· en· W2062440467 on OpenAlexaff
C. Freda, Don R. Baker, Cláudia Romano, Piergiorgio Scarlato

Bibliographic record

VenueGeological Society London Special Publications · 2003
Typearticle
Languageen
FieldPhysics and Astronomy
TopicNMR spectroscopy and applications
Canadian institutionsMcGill University
Fundersnot available
KeywordsGeologyDiffusionNatural (archaeology)GeochemistryThermodynamicsPaleontology

Abstract

fetched live from OpenAlex

Abstract Water diffusion experiments were performed on a trachytic melt from the Agnano-Monte Spina explosive eruption (Phlegrean Fields, South Italy). Experiments were run in a piston cylinder apparatus at 1 GPa pressure, at temperatures from 1373 to 1673 K and for durations of 0 to 255 s, using the diffusion-couple technique. Water concentration profiles were measured by Fourier transform infrared spectrometry. Water diffusion coefficients at different temperatures and water concentrations were calculated from the total water profiles, using the Boltzmann-Matano technique. Over the investigated range of temperatures and water concentrations, the diffusivity of water in potassic melts ( D water ), m 2 /s can be described by Arrhenius equations that can be generalized for water concentrations between 0.25 and 2 wt% as follows: D water = exp ( − 11.924 − 1.003 ln C H 2 O ) exp ( − ( exp ( 11.836 − 0.139 ln C H 2 O ) ) RT ) where C H 2 O is the water concentration in wt%, R is 8.3145 (J K −1 mol. −1 ) and T is the temperature in Kelvin. Water diffusivities in trachytic melts were compared with water diffusivities in rhyolitic and basaltic melts. The activation energies for water diffusivity in trachyte and basalt are comparable, and higher than the haplogranitic melt. This results in a convergence of water diffusion coefficients in all melts at lower (magmatic) temperatures.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.010
GPT teacher head0.279
Teacher spread0.269 · 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 source (direct Gemma or distilled Codex), 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

Citations46
Published2003
Admission routes1
Has abstractyes

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