MétaCan
Menu
Back to cohort
Record W2110106189 · doi:10.1109/mwscas.1994.519061

An artificial intelligent instructor for electrical circuits

2002· article· en· W2110106189 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

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicAI-based Problem Solving and Planning
Canadian institutionsQueen's University
Fundersnot available
KeywordsResistorCapacitorElectrical networkComputer scienceElectronic circuitInductorElectrical engineeringElectrical elementPrologPull-up resistorInferenceElectronic engineeringArtificial intelligenceEngineeringVoltageDigital electronics

Abstract

fetched live from OpenAlex

An AI system is implemented by using the Prolog language. The system teaches students basic electrical circuit theory by generating problems. The circuits consist of resistors, capacitors, inductors and an ac supply. They could also consist of resistors connected to a dc power supply. The system reasoning starts by generating a random hypothesised answer and works backwards toward the supporting evidence to generate a suitable electrical circuit. A central problem lies in establishing an inference mechanism from the different rules that control the electrical circuit. The system answers the questions and explains the steps for the solution.

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: Methods · Consensus signal: none
Teacher disagreement score0.968
Threshold uncertainty score0.342

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 science0.0010.000
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 head0.056
GPT teacher head0.268
Teacher spread0.212 · 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

Quick stats

Citations7
Published2002
Admission routes1
Has abstractyes

Explore more

Same topicAI-based Problem Solving and PlanningFrench-language works237,207