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Record W2531224527 · doi:10.1115/imece2001/htd-24332

Predictions of Carbon Fluxes During a Low Pressure Carburizing Treatment

2001· article· en· W2531224527 on OpenAlexaff
Philippe Jacquet, Daniel R. Rousse, Clemente C. Ibarra

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMicrostructure and Mechanical Properties of Steels
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsCarburizingAcetyleneMaterials scienceIndentation hardnessAtmosphere (unit)PropaneCarbon fibersCarbon dioxideMetallurgyTube (container)MethaneComposite materialThermodynamicsChemistryComposite numberMicrostructure

Abstract

fetched live from OpenAlex

Abstract This paper presents the qualitative assessment of a novel device developed for the regulation of carburizing processes in industrial vacuum furnaces. The proposed device involves a U-shaped thin wall iron tube: the outside surface of the tube is exposed to the carburizing atmosphere simultaneously with the workload, while the decarburizing gas mixture (here H2 + H2O) is circulated inside the tube. The outflow of decarburizing mixture is then continuously analyzed and eventually permits to determine the carbon potential and the transfer coefficient at the interface between the carburizing atmosphere and the workloads. To assess the principle, the probe has been used to compare the carburizing powers of different gases (propane, methane, ethylene, and acetylene) for a specific set of parameters. The results reported here, compared with microhardness and micrographies of the samples, indicate that the probe can indeed be used to carry out this task.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 integrity0.0010.000
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 head0.007
GPT teacher head0.180
Teacher spread0.173 · 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 designSimulation or modeling
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

Citations0
Published2001
Admission routes1
Has abstractyes

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