MétaCan
Menu
Back to cohort
Record W2106864051 · doi:10.1109/psce.2006.296396

A Partnership Between the Electrical Power Industry and Academia to Address the Technical Talent Gap

2006· article· en· W2106864051 on OpenAlexaboutno aff
Leonard Fiume, Ilya Grinberg, Mohammed Safiuddin, Robert J. Zahm

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicExperimental Learning in Engineering
Canadian institutionsnot available
Fundersnot available
KeywordsGeneral partnershipWebcastDegree programPresentation (obstetrics)Electric powerPower (physics)The InternetEngineering managementDistance educationGovernment (linguistics)Engineering educationGridElectric lightBusinessEngineeringComputer scienceElectrical engineeringMultimediaSociologyWorld Wide WebMedical educationPedagogy

Abstract

fetched live from OpenAlex

National Grid partnered with the University at Buffalo to develop a Master of Engineering Degree program to provide a professional degree education tailored to the needs of employees working full time. The target audience for the program was new engineers just hired into the company that needed a background in electric power, as well as existing engineers that wanted to earn an advanced degree, obtain PDH credits and/or increase their knowledge of electric power. The program was offered via distance learning to reach a larger audience and the program had participation from utility engineers from National Grid, Energy East, New York Power Authority, and Ontario Hydro. The distance learning using Webcasting tools had the additional benefit of allowing guest speakers and instructors from anywhere with Internet access to do a presentation for one of the classes

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)0.011
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.016
Threshold uncertainty score0.058

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.001
Scholarly communication0.0060.005
Open science0.0010.010
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0160.004

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 head0.017
GPT teacher head0.266
Teacher spread0.250 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations2
Published2006
Admission routes1
Has abstractyes

Explore more

Same topicExperimental Learning in EngineeringFrench-language works237,207