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Record W2075339654 · doi:10.1179/026708303225002848

Warm rolling behaviour of low carbon steels

2003· article· en· W2075339654 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.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueMaterials Science and Technology · 2003
Typearticle
Languageen
FieldEngineering
TopicMicrostructure and Mechanical Properties of Steels
Canadian institutionsnot available
FundersNatural Resources CanadaNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceMetallurgyChromiumBoronAnnealing (glass)Carbon fibersCarbon steelShear (geology)Composite materialCorrosionComposite number

Abstract

fetched live from OpenAlex

Warm (ferritic) rolling can be a low cost method of producing sheet steel products. However, for steels containing solute carbon, microstructural development during processing is affected by dynamic strain aging (DSA). This can significantly weaken the {111} texture formed during annealing, thus resulting in products with poor formabilities. It is known that the DSA behaviour can be modified by the addition of elements such as boron and chromium. Experimental low carbon (LC) steels with various additions of chromium, boron and phosphorus were warm rolled and their behaviour compared with that of a standard LC material. It was found that these additions promote the formation of shear bands under warm rolling conditions, thus resulting in a stronger {111} recrystallisation texture than that of the unmodified LC steel.

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 score0.001
Threshold uncertainty score0.275

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 head0.005
GPT teacher head0.185
Teacher spread0.180 · 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