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Record W1968830689 · doi:10.1109/tdc.2014.6863414

Risk-based asset management optimization

2014· article· en· W1968830689 on OpenAlexaffabout
Robert Otal, Alexander Bakulev

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPower System Reliability and Maintenance
Canadian institutionsHydro One (Canada)
Fundersnot available
KeywordsAsset (computer security)Risk analysis (engineering)IT asset managementAsset managementScope (computer science)BusinessRisk managementOrder (exchange)Investment (military)Present valueService (business)FinanceActuarial scienceComputer scienceComputer security

Abstract

fetched live from OpenAlex

This paper describes a risk-based approach developed by Toronto Hydro-Electric System Limited (THESL) for optimizing and justifying asset management (AM) decisions related to medium-voltage electrical distribution systems. This approach balances the risk of in-service asset failures against the capital expenditures required to mitigate these risks in order to identify the optimal scope and timing of investments into aging infrastructure. At a broader level, this approach has been applied as part of a business case evaluation process, whereby the reductions in total cost of ownership — including asset risk, non-asset risk and maintenance costs, are compared to the overall investment costs to maximize the net present value (NPV) of investments.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.962
Threshold uncertainty score0.198

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 head0.002
GPT teacher head0.163
Teacher spread0.160 · 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

Citations2
Published2014
Admission routes2
Has abstractyes

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