MétaCan
Menu
Back to cohort
Record W2163633975 · doi:10.1002/sca.4950270305

Porosity determination of ceramic materials by digital image analysis - a critical evaluation

2006· article· en· W2163633975 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueScanning · 2006
Typearticle
Languageen
FieldEngineering
TopicHigh-Temperature Coating Behaviors
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsPorosityMaterials scienceCeramicCubic zirconiaDigital image analysisDigital imageWork (physics)Image analysisComposite materialMineralogyImage (mathematics)Image processingComputer scienceMechanical engineeringComputer visionChemistry

Abstract

fetched live from OpenAlex

Measuring the porosity of materials by digital image analysis of micrographs is a well-established and convenient method for the testing of metallic samples. However, when applied to ceramic materials, this method has been shown to be much less reliable and poorly reproducible. The purpose of this present work is to clarify the reason for this deficiency, involving many porosity measurements, performed on plasma-sprayed zirconia, under systematically varied microscopic imaging conditions, and the porosities being calculated using various evaluation methods. Comparison between of the results has shown that the present state of the image analysis method is not satisfactory for absolute porosity measurements on ceramic materials. It can be useful as a convenient tool for comparative measurements, however, if the imaging conditions maintained in the microscope and the evaluation method are held to be exactly identical.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.086
Threshold uncertainty score0.459

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.267
Teacher spread0.260 · 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