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Record W1599465458 · doi:10.2172/991558

Indian Renewable Energy Status Report: Background Report for DIREC 2010

2010· report· en· W1599465458 on OpenAlexfundno aff
Dr Sapna Arora, Sarah Busche, Shannon Cowlin, Tobias Engelmeier, J Jaritz, Anelia Milbrandt, Shawn Wang

Bibliographic record

Venuenot available
Typereport
Languageen
FieldEnvironmental Science
TopicEnergy and Environment Impacts
Canadian institutionsnot available
FundersH2020 European Research CouncilOffice of Energy EfficiencyNational Renewable Energy LaboratoryDanish International Development AgencyEuropean CommissionGlobal Environment FacilityU.S. Department of StateOffice of Energy Efficiency and Renewable EnergyCanada Excellence Research Chairs, Government of CanadaU.S. Department of Energy
KeywordsRenewable energySoftware deploymentBusinessGovernment (linguistics)Wind powerScale (ratio)Environmental economicsNatural resource economicsRenewable resourceEnvironmental resource managementEconomicsEngineeringGeography

Abstract

fetched live from OpenAlex

India has great potential to accelerate use of endowed renewable resources in powering its growing economy with a secure and affordable energy supply. The Government of India recognizes that development of local, renewable resources will be critical to ensure that India is able to meet both economic and environmental objectives and has supported the development of renewable energy through severalpolicy actions. This paper describes the status of renewable energy in India as of DIREC 2010. It begins by describing the institutional framework guiding energy development in India, the main policy drivers impacting energy, and the major policy actions India has taken that impact renewable energy deployment. The paper presents estimates of potential for wind, solar, small hydro, and bioenergyand the deployment of each of these technologies to date in India. The potential for India to meet both large-scale generation needs and provide access to remote, unelectrified populations are covered. Finally, the enabling environment required to facilitate rapid scale of renewables is discussed, including issues of technology transfer and the status of financing in India.

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.141
Threshold uncertainty score0.280

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.006
Science and technology studies0.0010.000
Scholarly communication0.0030.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0170.015

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 head0.029
GPT teacher head0.271
Teacher spread0.242 · 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

Citations60
Published2010
Admission routes1
Has abstractyes

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