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Record W4402396862 · doi:10.24908/iqurcp18075

Edge Detection for Molten Metal Level in Pyrometalurgical Furnaces

2024· article· en· W4402396862 on OpenAlexvenueno aff
John Timpson

Bibliographic record

VenueInquiry Queen s Undergraduate Research Conference Proceedings · 2024
Typearticle
Languageen
FieldEngineering
TopicMetallurgical Processes and Thermodynamics
Canadian institutionsnot available
Fundersnot available
KeywordsMolten metalEnhanced Data Rates for GSM EvolutionMetallurgyMaterials scienceComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

This report has found a prospective analytical model for an edge response of a transmit-receive eddy current coil. A probe of this design could be used to determine in real time the molten metal height and remaining wall thickness within a pyrometallurgical furnace. This work heavily uses previously published methods to find a possible expression for the change in mutual coil impedance resulting from a right-angled edge. An advancement in the method includes using the Lorentz Reciprocity relations for a transmit-receive system to calculate impedance change. Awaiting the completion of the model, an expected response has also been modelled in a finite element method software, which will be used to verify the analytical model. Also, an experiment has been designed to be able to validate both models. What remains to be completed is a successful code implementation of the mathematics, to produce results. While analytical solutions to the equations have been obtained, implementation of coded solutions remains incomplete. Some methods for improving the code will be to better condition the matrices described in the linear algebra interface between the three regions, as well as making further improvements on the root-finding code.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.137
GPT teacher head0.365
Teacher spread0.227 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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Same venueInquiry Queen s Undergraduate Research Conference ProceedingsSame topicMetallurgical Processes and ThermodynamicsFrench-language works237,207