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Record W3216267736 · doi:10.1007/s11665-021-06418-4

Different Phenomena Encountered during Dilatometry of Low-density Steels

2021· article· en· W3216267736 on OpenAlexaff
Pello Jimbert, Teresa Guraya, Idurre Kaltzakorta, Teresa Gutiérrez, Roberto Elvira, Leili Tafaghodi Khajavi

Bibliographic record

VenueJournal of Materials Engineering and Performance · 2021
Typearticle
Languageen
FieldEngineering
TopicMicrostructure and Mechanical Properties of Steels
Canadian institutionsMcMaster University
FundersEuropean Social FundEuropean Regional Development FundEuskal Herriko UnibertsitateaEusko Jaurlaritza
KeywordsMaterials scienceHomogeneity (statistics)ManganeseMetallurgyDiffusionComposite materialThermodynamics

Abstract

fetched live from OpenAlex

Abstract In recent decades, highly alloyed low-density steels are being developed to reduce the weight of different automotive parts. Dilatometry can be a very useful experimental technique to understand phase transformations during heating or cooling of new low-density steel alloys. When performing dilatometry measurements some assumptions are made such as the homogeneity of the sample material tested during the experiment. In this study, dilatometry tests were performed for two different low-density steels, and the variations of the composition between the surface and the inner part of the sample were analyzed. The migration of manganese by diffusion from the interior of the samples and finally its evaporation on the surface under vacuum were observed. This compositional gradient generated in the samples may influence the veracity and interpretation of the results obtained in dilatometry when working with high manganese steel alloys. The detachment of surface grains created by this compositional change near the surface of the samples is also investigated.

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)0.000
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 score0.009
Threshold uncertainty score0.471

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 head0.004
GPT teacher head0.163
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

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

Citations5
Published2021
Admission routes1
Has abstractyes

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