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Record W2034402516 · doi:10.1016/s0026-0657(01)80362-3

Comparison of non-destructive flaw detection methods in PM

2001· article· en· W2034402516 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

VenueMetal Powder Report · 2001
Typearticle
Languageen
FieldEngineering
TopicNon-Destructive Testing Techniques
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsAlloyMaterials scienceDissolutionKineticsLeaching (pedology)MetallurgyFEALPrecipitationSiliconChemical engineeringYield (engineering)CrackingPhase (matter)Refining (metallurgy)Composite materialIntermetallicChemistryOrganic chemistry

Abstract

fetched live from OpenAlex

The kinetics of refinement of Fe-Si alloys by acid leaching is affected by their structural state. Two different phase constitutions were studied to access this effect: an industrial alloy with a low AlCa ratio and a laboratory-prepared alloy with a high Al content. The first contains silicon, FeSi2 (tet.), FeSi2 (orth.), beyond a quaternary phase Fe-Al-Si-Ca (Caalsifer), CaAi2Si1.5, CaSi2, Al-Fe-Si as minor phases. The precipitation of Al-Fe-Si phases is induced in the second by the high AlCa ratio. In a previous study, leaching experiments by a two-step procedure have shown that Ca-Al-Si, Caalsifer and Al-Fe-Si phases are more soluble. The Cracking Shrinking Model (CSM) was applied to explain the behaviour of the industrial alloy. In the present study, it is shown that the same model applies to the kinetics of dissolution of the laboratory-prepared alloy and also, that an adequate control of FeAl vs. SiCa ratios may improve the attainable refining yield.

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)0.001
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 score0.172
Threshold uncertainty score0.890

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.040
GPT teacher head0.381
Teacher spread0.341 · 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