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Record W4403612966 · doi:10.1177/03019233241291653

The effect of non-uniform skelp temperature on X70 steel coil cooling

2024· article· en· W4403612966 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.

Bibliographic record

VenueIronmaking & Steelmaking Processes Products and Applications · 2024
Typearticle
Languageen
FieldEngineering
TopicMicrostructure and Mechanical Properties of Steels
Canadian institutionsEVRAZ (Canada)University of Alberta
Fundersnot available
KeywordsElectromagnetic coilMaterials scienceMetallurgyComposite materialMechanical engineeringElectrical engineeringEngineering

Abstract

fetched live from OpenAlex

This study explored the effect of varying steel skelp temperature (500 °C–600 °C) on the subsequent temperature-time ( T- t) profile during and after coiling of low C (0.05 wt. %) X70 microalloyed steels. Three industrially produced steel coils with similar rough rolling temperatures (∼1050 °C) and finish rolling temperatures (∼850 °C) but different coiling temperatures (500 °C–600 °C) and different skelp thicknesses (19 mm and 25 mm) were studied. The surface temperature of each coil was measured using an infrared video camera. A three-dimensional heat transfer model was developed to predict the T- t profiles. A lower (∼60 °C) initial skelp temperature near the head and tail of the skelp led to a reduced coil temperature. A higher (∼50 °C) temperature near the head and tail region of the skelp resulted in a more uniform coil temperature (a difference of ∼10 °C instead of ∼50 °C).

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.619
Threshold uncertainty score0.739

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.224
Teacher spread0.218 · 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