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Record W2061944692 · doi:10.1063/1.3597673

Disaggregating meteorites by automated freeze thaw

2011· article· en· W2061944692 on OpenAlexaff
C. R. J. Charles

Bibliographic record

VenueReview of Scientific Instruments · 2011
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstro and Planetary Science
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMeteoriteChondruleLiquid nitrogenChondriteMaterials scienceVialMineralogyGeologyChemistryAstrobiologyChromatographyPhysics

Abstract

fetched live from OpenAlex

An automated freeze-thaw (AFT) instrument for disaggregating meteorites is described. Meteorite samples are immersed in 18.2 MΩ water and hermetically sealed in a clean 30 ml Teflon vial. This vial and its contents are dipped between baths of liquid nitrogen and hot water over a number of cycles by a dual-stepper motor system controlled by LabView. Uniform and periodic intervals of freezing and thawing induce multiple expansions and contractions, such that cracks propagate along natural flaws in the meteorite for a sufficient number of AFT cycles. For the CR2 chondrite NWA801, the boundaries between different phases (i.e., silicates, metal, matrix) became progressively weaker and allowed for an efficient recovery of 500 individual chondrules and chondrule fragments spanning 0.2-4.7 mm diameters after 243 AFT cycles over 103.3 h. Further FT experiments on a basalt analog showed that the time required for freezing and thawing the same number of cycles can be reduced by a factor of ∼4.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.433
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0030.000

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 head0.018
GPT teacher head0.243
Teacher spread0.224 · 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 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

Citations1
Published2011
Admission routes1
Has abstractyes

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