MétaCan
Menu
Back to cohort

Macrosegregation of Alloying Elements in Hot Top of Large Size High Strength Steel Ingot

2016· article· en· W2551681543 on OpenAlexaff
Abdelhalim Loucif, D. Shahriari, Chunping Zhang, Mohammad Jahazi, Louis Philippe Lapierre-Boire, Rami Tremblay

Bibliographic record

VenueMaterials science forum · 2016
Typearticle
Languageen
FieldEngineering
TopicMetallurgical Processes and Thermodynamics
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsIngotHomogeneity (statistics)Materials scienceMetallurgyChemical compositionAlloyChemistry

Abstract

fetched live from OpenAlex

The chemical heterogeneities of alloying elements were evaluated in the hot top plus the top of a 40-ton ingot of as-cast high strength low alloy steel. The chemical compositions of small samples, taken from a slice cut along the longitudinal axis of the ingot, were obtained using mass spectroscopy. The chemical results were used to construct the chemical heterogeneity maps of C, Mn, Ni, Cr and Mo in the entire slice. The analyses of the different maps indicate the existence of positive segregation for all segregated elements except Ni where no segregation was observed. The most important macrosegregation was revealed in the centerline of the ingot. Carbon presents the highest degree of segregation whereas Mo presents the lowest one. In term of homogeneity degrees, Mn, Ni, Cr and Mo present better homogeneity than C whether in the top of the ingot or in the hot top.

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 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 score0.007
Threshold uncertainty score0.334

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.006
GPT teacher head0.229
Teacher spread0.223 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations11
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueMaterials science forumSame topicMetallurgical Processes and ThermodynamicsFrench-language works237,207