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Record W3114221509 · doi:10.18280/ijsdp.150821

Developing a Framework for Rural Electrification in India- Analysis of the Prospects of Micro-grid Solutions

2020· article· en· W3114221509 on OpenAlexvenueno aff
Archan Bhanja, Anil Kumar, Anshuman Gupta, Arijit K. Gupta, Avishek Ghosal, Subhashis Mukherjee, Saswata Chaudhury

Bibliographic record

VenueInternational Journal of Sustainable Development and Planning · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicEnergy and Environment Impacts
Canadian institutionsnot available
Fundersnot available
KeywordsRural electrificationMicrogridStakeholderElectrificationSample (material)Environmental economicsElectricityGridBusinessGeographyEngineeringEconomics

Abstract

fetched live from OpenAlex

The present research examines the perceptions of rural consumers towards the microgrid and rural electrification (RE) based solutions and develops a framework for improving the establishment of rural RE based microgrid solution captured from a case study in the Kalimpong district of North Bengal. The study adopted a mixed methodology study design includes the qualitative and quantitative aspects of the study through stakeholder interviews and questionnaire-based primary survey of the sample households. The perceptions received from sample households were validated with the response from other stakeholders, including academicians, researchers and sectoral experts. Data were analysed using structural equation modelling (SEM). The findings of the present research indicate that to accelerate the socio-economic development of the hilly terrains of North Bengal region, multi-provider licensed based microgrid preferably with renewable-based is a preferred mode which ensures reliable and affordable access of electricity.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.166
Threshold uncertainty score0.200

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 head0.018
GPT teacher head0.253
Teacher spread0.234 · 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 designObservational
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

Citations3
Published2020
Admission routes1
Has abstractyes

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