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Record W2101816493 · doi:10.1109/39.920959

Front Cover

2001· article· en· W2101816493 on OpenAlexaff
R. Billington, Sarfraz Ali, G. Wacker

Bibliographic record

VenueIEEE Power Engineering Review · 2001
Typearticle
Languageen
FieldEngineering
TopicPower System Reliability and Maintenance
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsPurchasing power parityCurrencyReliability (semiconductor)PurchasingPurchasing powerEconomicsBusinessValue (mathematics)Actuarial scienceEconometricsPower (physics)Computer scienceMonetary economicsOperations managementMacroeconomicsExchange rate

Abstract

fetched live from OpenAlex

Many countries have conducted studies to determine the monetary impact of power system outages on their customers. These studies have been conducted for different customer classes and have used a wide range of survey or study techniques. The data is being used to examine reliability levels and criteria and to provide input to planning and operating decisions. One issue often raised is the question of comparable reliability criteria in different systems and different countries. The customer interruption costs (CIC) in different countries can be compared by converting the CIC data using purchasing power parity (PPP). A PPP estimate reflects the purchasing power of the inhabitants of a country and depends on market value. The effect of frequent currency fluctuations due to artificial reasons are eliminated in the PPP estimate. In the PPP approach, the prices of goods and services are internationally arbitraged so that the cost of a standard market basket is the same in all countries when measured in terms of a common currency.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.077
Threshold uncertainty score0.109

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0030.002
Open science0.0010.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.9230.897

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 head0.007
GPT teacher head0.200
Teacher spread0.194 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

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

Citations7
Published2001
Admission routes1
Has abstractyes

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