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Record W1990316863 · doi:10.1179/174328408x307247

Effect of microstructure and chemical composition on dynamic factor of high strength steels

2008· article· en· W1990316863 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

VenueMaterials Science and Technology · 2008
Typearticle
Languageen
FieldMaterials Science
TopicHigh-Velocity Impact and Material Behavior
Canadian institutionsMcGill University
FundersNetworks of Centres of Excellence of Canada
KeywordsMaterials scienceBainiteMicrostructureFerrite (magnet)MartensiteAustenitePearliteMetallurgyBeta ferriteComposite material

Abstract

fetched live from OpenAlex

The high strain rate properties of high strength steels with various microstructures and static strength levels were studied by means of split Hopkinson bar apparatus in shear punch mode. The as received cold rolled sheet steels were subjected to a variety of heat treatment conditions to produce several different microstructures, namely ferrite plus pearlite (F+P), ferrite plus bainite (F+B), ferrite plus martensite (F+M) and ferrite plus bainite and retained austenite (F+B+RA). According to the variation of dynamic factor (ratio of dynamic to static strength) with static strength, two groups of microstructures with two distinct behaviours were identified, i.e. classic dual phase (ferrite plus martensite) and multiphase (including ferrite–pearlite, ferrite–bainite, etc.). It was also observed that the general dependence of microstructure on the dynamic factor was strongly influenced by chemical composition in the case of ferrite plus martensite microstructures.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.845

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 studies0.0000.002
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 head0.005
GPT teacher head0.249
Teacher spread0.243 · 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