MétaCan
Menu
Back to cohort
Record W1492810508 · doi:10.1596/978-1-4648-0341-3

Power for All: Electricity Access Challenge in India

2014· book· en· W1492810508 on OpenAlexaboutno aff
Sudeshna Ghosh Banerjee, Douglas F. Barnes, Hussain A. Samad

Bibliographic record

VenueThe World Bank eBooks · 2014
Typebook
Languageen
FieldEnvironmental Science
TopicEnergy and Environment Impacts
Canadian institutionsnot available
Fundersnot available
KeywordsElectricityMains electricityRural areaRenewable energyQuarter (Canadian coin)PopulationBusinessElectricity retailingElectricity generationEconomic growthAgricultural economicsGeographyEconomicsElectricity marketPower (physics)EngineeringPolitical scienceElectrical engineering

Abstract

fetched live from OpenAlex

India has led the developing world in addressing rural energy problems. By late 2012, the national electricity grid had reached 92 percent of India s rural villages, about 880 million people. In more remote areas and those with geographically difficult terrain, where grid extension is not economically viable, off-grid solutions using renewable-energy sources for electricity generation and distribution have been promoted. The positive results of the country s rural energy policies and institutions have contributed greatly to reducing the number of people globally who remain without electricity access. Yet, owing mainly to its large population, India has by far the world s largest number of households without electricity. More than one-quarter of its population or about 311 million people, the vast majority of whom live in poorer rural areas, still lack an electricity connection; less than half of all households in the poorest income group have electricity. Among households with electricity service, hundreds of millions lack reliable power supply.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.025
Threshold uncertainty score0.084

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0020.001
Scholarly communication0.0050.004
Open science0.0010.003
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0250.009

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 head0.017
GPT teacher head0.249
Teacher spread0.232 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations12
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueThe World Bank eBooksSame topicEnergy and Environment ImpactsFrench-language works237,207