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Record W2101816493 · doi:10.1109/39.920959

Front Cover

2001· article· en· W2101816493 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

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.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.831
Threshold uncertainty score0.999

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)0.0010.002

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