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Record W4386425919 · doi:10.5151/2594-5300-39887

CAMPAIGN LIFE ASSESSMENT AND DESIGN IMPROVEMENT OF BASIC OXYGEN FURNACES

2023· article· en· W4386425919 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

VenueABM Proceedings · 2023
Typearticle
Languageen
FieldEngineering
TopicMetallurgical Processes and Thermodynamics
Canadian institutionsHatch (Canada)
Fundersnot available
KeywordsRoot causeReliability engineeringSteelmakingConvertersService lifeEngineeringRisk analysis (engineering)Service (business)Root cause analysisComputer scienceManufacturing engineeringAutomotive engineeringBusinessElectrical engineering

Abstract

fetched live from OpenAlex

PDF | Basic oxygen furnace converters in integrated steelmaking facilities are exposed to severe operating conditions, often beyond the original design conditions. Safety risks and unplanned shutdowns associated with failure of this equipment can impose significant costs and operational disruptions. Inspections, root cause analysis of damage, fitness-for-service assessment, and repair development are critical for the reliable operation of this equipment. Thermo-mechanical analysis can be performed using finite element analysis tools to more accurately quantify the extent of damage and identify the root cause of damage in this equipment. Understanding the mechanisms that influence the lifespan of refractory and converters provides the opportunity to identify design improvements. These improvements can extend the life of an existing converter or increase the life of a replacement converter. This methodology enables creative and well-engineered solutions to be developed to optimize the converter design and operation to meet the specific business and production needs of a steel plant.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.941
Threshold uncertainty score0.507

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 head0.020
GPT teacher head0.240
Teacher spread0.220 · 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