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Record W1589672968 · doi:10.1002/9780470310557.ch56

Nondestructive Characterization of Slip Cast SiC Fiber- and Whisker-Reinforced Alumina

2008· book-chapter· en· W1589672968 on OpenAlexaff
S. Karunanithy, Michael Falk

Bibliographic record

VenueCeramic engineering and science proceedings · 2008
Typebook-chapter
Languageen
FieldMaterials Science
TopicAdvanced ceramic materials synthesis
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsWhiskersMaterials scienceWhiskerSilicon monoxideComposite materialAgglomerateFourier transform infrared spectroscopySlip (aerodynamics)SinteringCharacterization (materials science)Homogeneity (statistics)Monocrystalline whiskerInfraredSilicon carbideSiliconChemical engineeringMetallurgyNanotechnologyOptics

Abstract

fetched live from OpenAlex

Nuclear magnetic resonance (NMR) imaging and Fourier transform infrared (FT-IR) spectrometry are used to identify physical and chemical flaws in alumina reinforced with SiC fibers or whiskers. Physical flaws such as agglomerates, hard inclusions, and open pores are detected in the green state of slip-cast composites by NMR imaging. The images provide an understanding of the homogeneity of distribution of fibers and whiskers in the unsintered ceramics. FT-IR studies of the fibers and whiskers, both by themselves and in the composites, are used to determine the extent of oxidation. This yields quantitative information on the chemical flaws caused by the oxidation of SiC to silica or silicon monoxide during the sintering of the composites.

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 score0.001
Threshold uncertainty score0.002

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 head0.008
GPT teacher head0.190
Teacher spread0.182 · 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

Citations5
Published2008
Admission routes1
Has abstractyes

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