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Effect of double-hit deformation on the onset of critical stress for the initiation of dynamic transformation

2020· dataset· en· W4240036599 on OpenAlexaff
Kanwal Chadha, Clodualdo Aranas, Zaid Ahmad, Mohammad Jahazi, J. G. Spray

Bibliographic record

VenueAuthorea · 2020
Typedataset
Languageen
FieldMaterials Science
TopicHigh-Velocity Impact and Material Behavior
Canadian institutionsÉcole de Technologie SupérieureUniversity of FrederictonUniversity of New Brunswick
Fundersnot available
KeywordsMaterials scienceCompression (physics)Deformation (meteorology)Stress (linguistics)Strain (injury)MetallurgyStrain rateTransformation (genetics)AlloyComposite materialStructural engineeringEngineeringChemistryMedicine

Abstract

fetched live from OpenAlex

Double-hit hot compression tests were carried on medium-carbon low-alloy steels using Gleeble 3800® thermomechanical simulator. The tests were performed at strain rates of 0.25 and 0.5 s-1 and temperatures of 1150 and 1200 °C with an interpass time of 5s. The onset of critical stress for dynamic transformation (DT) were detected using the double‐differentiation method. This metallurgical phenomenon was initiated under all testing conditions. The results show that the critical stress for DT increases with decrease in temperature and increase in strain 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.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.015
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.004
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0110.011

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 head0.031
GPT teacher head0.343
Teacher spread0.312 · 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 designBench or experimental
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
Published2020
Admission routes1
Has abstractyes

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