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Record W2469370220 · doi:10.1051/meca/2016003

To what extent do thermo physical properties of a metallurgical reactor affect the performance of a virtual sensor used for predicting the ledge profile?

2016· article· en· W2469370220 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

VenueMechanics & Industry · 2016
Typearticle
Languageen
FieldEngineering
TopicMetallurgical Processes and Thermodynamics
Canadian institutionsUniversité de Sherbrooke
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsInertiaMaterials scienceThermalRange (aeronautics)Phase (matter)Phase-change materialNuclear engineeringMechanical engineeringComputer scienceComposite materialEngineeringThermodynamicsPhysics

Abstract

fetched live from OpenAlex

A non-invasive virtual sensor is employed for the inverse prediction of the time-varying ledge profile that forms inside high-temperature metallurgical reactors filled with a load of phase change material (PCM). The virtual sensor is tested for thermo physical properties of the vessel wall and of the PCM that fall outside the range for which it was originally designed. The results are analyzed and presented in terms of the shift of key thermo physical properties from the reference case. Results indicate that the virtual sensor is more sensitive to the variation of the properties of the phase change material than that of the vessel walls. The virtual sensor response remains accurate for reactor loads of high thermal inertia. The virtual sensor may still be used for reactor loads of low thermal inertia provided that thermo physical properties of the PCM are well-known.

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)0.001
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 score0.740
Threshold uncertainty score0.388

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.022
GPT teacher head0.228
Teacher spread0.206 · 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