MétaCan
Menu
Back to cohort
Record W3087161084 · doi:10.3390/cryst10090836

Effect of Nitrogen Content on the Formation of Inclusions in Fe-5Mn-3Al Steels

2020· article· en· W3087161084 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueCrystals · 2020
Typearticle
Languageen
FieldEngineering
TopicMetallurgical Processes and Thermodynamics
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of CanadaArcelorMittal
KeywordsMaterials scienceNitrogenMetallurgyInclusion (mineral)Content (measure theory)Analytical Chemistry (journal)MineralogyChemistry

Abstract

fetched live from OpenAlex

The effect of N content on the characteristics and formation of inclusions in the Fe-5Mn-3Al steels was investigated in this study. Two synthetic steel melts were produced by two different methods—N2 gas purging and injecting—to introduce nitrogen into the melt. The N content of steel melt varied from 2 to 54 ppm. An increase in the N content to 47 ppm (for 533N-P) and 58 ppm (for 533N-I) increased the total amount of inclusions from 13 to 64 mm−2 and from 21 to 101 mm−2, respectively. The observed inclusions were Al2O3(pure), Al2O3-MnS, AlN(pure), AlN-MnS, AlON, AlON-MnS, and MnS. When the N content was less than 10 ppm, AlN-MnS inclusions were the primary type of inclusions and they formed as solidification products. With an increase in the N content, AlN(pure) inclusions became the dominant type of inclusions as AlN was stable in the liquid steel. These findings were confirmed by thermodynamic calculations. The influence of cooling rate on the types of inclusions was studied and a higher number of AlN-MnS inclusions were observed in samples with slow cooling rate.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.610
Threshold uncertainty score0.168

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 head0.026
GPT teacher head0.226
Teacher spread0.201 · 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