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Record W4394346607 · doi:10.6084/m9.figshare.20020665

The Analysis of the Microstructure and Mechanical Properties of Low Carbon Microalloyed Steels after Ultra Fast Cooling

2022· dataset· en· W4394346607 on OpenAlexaff
Yong Tian, Hongtao Wang, Yong Li, Zhaodong Wang, Guodong Wang

Bibliographic record

VenueFigshare · 2022
Typedataset
Languageen
FieldEngineering
TopicMicrostructure and Mechanical Properties of Steels
Canadian institutionsAlberta Health Services
Fundersnot available
KeywordsMicrostructureMaterials scienceMetallurgyCarbon fibersMicroalloyed steelComposite materialAustenite

Abstract

fetched live from OpenAlex

In this paper, two low carbon microalloyed steels, named as steel A and steel B, were fabricated by ultra fast cooling (UFC). In both steels, the microstructures containing quasi polygonal ferrite (QF), acicular ferrite (AF) and granular bainite (GB) can be obtained by UFC process. The amount of AF in steel B is more than that in steel A. The size and distribution of precipitates (Nb/Ti carbonitrides) in steel B are finer and more dispersed than those of in steel A due to relatively low finish cooling temperature. The mechanical properties of both steels are effectively enhanced by UFC process. UFC process produces low-temperature transformation microstructures containing a significant amount of AF. The mechanical properties of steel B were more satisfactory than those of steel A due to the finer average grain size, the greater amount of the volume fractions and smaller size of secondary phases.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.020
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.004
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0100.011

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 head0.009
GPT teacher head0.183
Teacher spread0.174 · 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 designNot applicable
Domainnot available
GenreDataset

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
Published2022
Admission routes1
Has abstractyes

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