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Record W2144606896 · doi:10.1109/fie.2004.1408522

Work in progress - virtual laboratory with a remote control instrumentation component

2005· article· en· W2144606896 on OpenAlexaffabout
C. Ciubotariu, G.C. Hancock

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicExperimental Learning in Engineering
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsOscilloscopeInstrumentation (computer programming)Computer scienceVirtual instrumentationDigital electronicsInterface (matter)Virtual LaboratoryComponent (thermodynamics)Computer hardwareElectronic circuitElectrical engineeringEngineeringMultimediaSoftwareOperating systemVoltage

Abstract

fetched live from OpenAlex

The virtual laboratory of the Department of Electrical and Computer Engineering of the University of Calgary comprises all laboratory components of the department, with two locations already developed for long distance education: a remotely controlled instrumentation laboratory, SelfLab@Home, and a digital design experiments collection where the operation of the basic digital circuits is simulated with HTML applets. The students are invited to take a virtual visit of the laboratory rooms in which they perform course required experiments in order to easier locate the devices to be used as well as operate them appropriately. The main objective of SelfLab@Home was to become a self-paced remotely accessed training site for the use of four basic laboratory devices: oscilloscope, waveform generator, DMM and a power supply. The digital devices experiments demonstrate the logic operation of digital circuits and a remotely controlled traffic light with its computer visual interface application.

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)0.006
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.046
Threshold uncertainty score0.154

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0050.003
Open science0.0040.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0460.028

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 head0.003
GPT teacher head0.194
Teacher spread0.191 · 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 designBench or experimental
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

Citations5
Published2005
Admission routes2
Has abstractyes

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