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Record W2888034474 · doi:10.2991/iwci-18.2018.4

Imitative Modelling of Electromagnetic Safety Conditions in Smart Power Supply Systems

2018· article· en· W2888034474 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

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicTransportation Systems and Safety
Canadian institutionsTransport Canada
Fundersnot available
KeywordsPower (physics)Smart powerComputer scienceAutomotive engineeringElectrical engineeringEngineeringPhysics

Abstract

fetched live from OpenAlex

The implementation of intelligent railway power supply system requires the development of computer technologies for modeling modes and electromagnetic fields determining the conditions for electromagnetic safety. Such technologies are implemented based on the methods developed at the Irkutsk State Transport University. The formation of intelligent power supply systems will solve the following important tasks: provision of high reliability of power supply for traction of trains, as well as for non-traction and non-transport consumers; increase of electromagnetic safety; minimization of energy losses and operating costs of traction power networks; improvement of electricity quality in traction power networks, as well as in the interface regions with the supply electric power system. The article presents the technology of electromagnetic environment simulation modeling on alternating current railway. An example of calculations is considered. The amplitude values of the magnetic field strength vary with the average size of train movement from several amperes per meter to 60 A/m, the electric field strength varies comparatively little.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.972
Threshold uncertainty score0.321

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 head0.019
GPT teacher head0.236
Teacher spread0.217 · 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

Quick stats

Citations9
Published2018
Admission routes1
Has abstractyes

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