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Record W3158807423 · doi:10.1155/2021/6664115

Layout Methods for Integrated Energy Supply Service Stations from the Perspective of Combination Optimization

2021· article· en· W3158807423 on OpenAlexvenueno aff
Zhenjun Zhu, Chaoxu Sun, Yudong He, Jiayan Shen, Jingrui Sun

Bibliographic record

VenueJournal of Advanced Transportation · 2021
Typearticle
Languageen
FieldEngineering
TopicElectric Vehicles and Infrastructure
Canadian institutionsnot available
FundersNatural Science Research of Jiangsu Higher Education Institutions of ChinaNanjing Forestry University
KeywordsEnergy supplyComputer scienceService (business)Perspective (graphical)Energy (signal processing)Plan (archaeology)Operations researchRationalityEngineeringBusiness

Abstract

fetched live from OpenAlex

Integrated energy supply service stations (IES) are a new type of transportation energy infrastructure offering the advantages of comprehensive functions and intensive land use while providing more convenient and efficient energy supply services. Through the analysis of service station characteristics, this study regards the IES as a spatially superimposed combination of various energy supply services, proposes a layout method from the perspective of combination optimization, and establishes a station optimization model for energy supply stations. This method aims to further coordinate and optimize the combination of various energy supply stations to achieve global optimization of the energy supply service system. Finally, this study uses a hypothetical situation for example analysis to verify the validity and rationality of the method. The layout plan proposed in this study has important theoretical and practical significance for how to achieve the optimal layout of an IES.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.006
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.006
GPT teacher head0.261
Teacher spread0.255 · 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 source (direct Gemma or distilled Codex), 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

Citations4
Published2021
Admission routes1
Has abstractyes

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