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Record W2111272836 · doi:10.1109/tdc.2006.1668721

Project Evaluation and Selection at BC Hydro

2006· article· en· W2111272836 on OpenAlexaff
R. Zucker

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPower System Reliability and Maintenance
Canadian institutionsBC Hydro (Canada)
Fundersnot available
KeywordsReliability (semiconductor)Index (typography)Customer satisfactionSelection (genetic algorithm)Duration (music)Computer scienceReliability engineeringProcess (computing)Operations researchEngineeringBusinessMarketingArtificial intelligencePower (physics)

Abstract

fetched live from OpenAlex

Summary form only given. Electric utilities are on a never ending quest to attain higher levels of performance for increasingly lower costs. An important part of this effort is to define meaningful definitions of performance. For distribution reliability typical approaches use reliability indices such as the system average interruption frequency index (SAIFI), the system average interruption duration index (SAIDI), and the customer average interruption duration index (CAIDI). Unfortunately, such measures are weakly correlated to customer expectations and customer satisfaction. Therefore, project evaluation and selection processes based on these measures may not be sufficient, at least from the customer perspective. At BC Hydro, performance targets are differentiated for different distribution circuits based on customer mix, geographic characteristics, and the customer expectations relates to the specific mix of geography and customer type. Such differentiation results in performance metrics that are more strongly linked to customer expectations, and serve as a solid basis for the project evaluation and selection process. This presentation describes the efforts at BC Hydro for developing performance targets, and the use of these performance targets in project evaluation and selection

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 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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.689
Threshold uncertainty score0.166

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 science0.0000.000
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 head0.009
GPT teacher head0.224
Teacher spread0.215 · 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 teacher head, 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
Published2006
Admission routes1
Has abstractyes

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