MétaCan
Menu
Back to cohort
Record W2148237494 · doi:10.1109/20.877763

A paradigm for the non-routine design of electromagnetic devices using a case based reasoning approach

2000· article· en· W2148237494 on OpenAlexaff
Duncan Lowther, Quoc Bao Vo

Bibliographic record

VenueIEEE Transactions on Magnetics · 2000
Typearticle
Languageen
FieldComputer Science
TopicAI-based Problem Solving and Planning
Canadian institutionsMcGill University
Fundersnot available
KeywordsComputer scienceSet (abstract data type)Programming language

Abstract

fetched live from OpenAlex

The use of optimization techniques to satisfy a set of specifications Is becoming more common. However, these processes may fail if the initial device, when considered with the specifications is "over-constrained." This paper proposes an approach to identifying this form of failure through the use of Case-Based Reasoning and three inter-related models of the device (structural, behavioral and mathematical). When a potential failure is found, the system makes structural changes to the device to bypass the failure.

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)0.013
metaresearch head score (Gemma)0.012
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 score0.013
Threshold uncertainty score0.068

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.012
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.003
Bibliometrics0.0040.002
Science and technology studies0.0020.011
Scholarly communication0.0080.008
Open science0.0090.004
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.0090.002

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 head0.034
GPT teacher head0.253
Teacher spread0.219 · 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

Citations9
Published2000
Admission routes1
Has abstractyes

Explore more

Same venueIEEE Transactions on MagneticsSame topicAI-based Problem Solving and PlanningFrench-language works237,207