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Record W1987028643 · doi:10.1107/s0909049509048225

The contribution of synchrotron X-ray computed microtomography to understanding volcanic processes

2010· article· en· W1987028643 on OpenAlexaff
Margherita Polacci, Lucia Mancini, Don R. Baker

Bibliographic record

VenueJournal of Synchrotron Radiation · 2010
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced X-ray Imaging Techniques
Canadian institutionsMcGill University
Fundersnot available
KeywordsVolcanoGeologySynchrotronBasaltSynchrotron radiationMineralogyVoid (composites)PetrologyEarth scienceMaterials scienceGeochemistryOpticsPhysics

Abstract

fetched live from OpenAlex

A series of computed microtomography experiments are reported which were performed by using a third-generation synchrotron radiation source on volcanic rocks from various active hazardous volcanoes in Italy and other volcanic areas in the world. The applied technique allowed the internal structure of the investigated material to be accurately imaged at the micrometre scale and three-dimensional views of the investigated samples to be produced as well as three-dimensional quantitative measurements of textural features. The geometry of the vesicle (gas-filled void) network in volcanic products of both basaltic and trachytic compositions were particularly focused on, as vesicle textures are directly linked to the dynamics of volcano degassing. This investigation provided novel insights into modes of gas exsolution, transport and loss in magmas that were not recognized in previous studies using solely conventional two-dimensional imaging techniques. The results of this study are important to understanding the behaviour of volcanoes and can be combined with other geosciences disciplines to forecast their future activity.

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)0.001
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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
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 head0.007
GPT teacher head0.260
Teacher spread0.253 · 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

Citations48
Published2010
Admission routes1
Has abstractyes

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