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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 OpenAlex
Dr Sapna Arora, Sarah Busche, Shannon Cowlin, Tobias Engelmeier, J Jaritz, Anelia Milbrandt, Shawn Wang

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.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

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.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
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 score0.052
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0210.000

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 head0.029
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

Quick stats

Citations60
Published2010
Admission routes1
Has abstractyes

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