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Record W4249756170 · doi:10.5383/swes.8.01.002

Effect of Temperature and Time of Carburizing Treatment on the Structure and the Hardness of Steel 20MC4

2016· article· en· W4249756170 on OpenAlex
Younès Benarioua

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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueInternational Journal of Sustainable Water and Environmental Systems · 2016
Typearticle
Languageen
FieldEngineering
TopicMetal and Thin Film Mechanics
Canadian institutionsnot available
FundersAgence Thématique de Recherche en Science et Technologie
KeywordsCarburizingMaterials scienceAusteniteMetallurgyMartensiteHardnessHardening (computing)Indentation hardnessCarbon steelDiffractionIndentationPrecipitation hardeningComposite materialMicrostructureCorrosion

Abstract

fetched live from OpenAlex

Carburizing technique has recently been developed to engineer the surfaces of the low steels for combined improvement in wear and fatigue resistance. The resultant carburized surface region is characterized by the high saturation of carbon in austenite lattices of steel. The duration and temperature of carburising surface hardening treatment can be chosen in agreement with the thermal treatment for obtaining optimal bulk hardness in the precipitation hardening steel. Characterization point of view structural and mechanical of the samples using X-ray diffraction, optical microscopy and micro indentation testing was then introduced in this work. It was found that the incorporation of carbon resulted in a hardened additional compounds consisting of a combination of martensite and expanded austenite.

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 score0.003
Threshold uncertainty score0.136

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 head0.002
GPT teacher head0.162
Teacher spread0.159 · 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