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Record W2112686536 · doi:10.5539/eer.v1n1p175

Innovation and Technology Management in Wind Energy Cluster

2011· article· en· W2112686536 on OpenAlexvenueno aff
Wagner Sousa de Oliveira, António Jorge Fernandes

Bibliographic record

VenueEnergy and Environment Research · 2011
Typearticle
Languageen
FieldDecision Sciences
TopicInnovation Diffusion and Forecasting
Canadian institutionsnot available
FundersFundação de Amparo à Pesquisa e ao Desenvolvimento Científico e Tecnológico do MaranhãoUniversidade de Aveiro
KeywordsWind powerIncentiveEnergy securityEnvironmental economicsCluster (spacecraft)Field (mathematics)Computer scienceValue (mathematics)Energy managementIndustrial organizationBusinessPower (physics)Energy (signal processing)Risk analysis (engineering)Renewable energyEconomics

Abstract

fetched live from OpenAlex

Innovation and technology management in wind energy industry is driven by policies and incentives due to their inherent characteristics such as high upfront costs, lack of level playing field but distinct advantages from energy security, environmental and social considerations. This paper makes an analysis of innovation and technology management in wind power industry, focusing on value chain and the interaction of technology and markets for contributing to recommendations on technology policy and management. This paper proposes a framework for analyzing the initial stage of diffusion of wind power systems, combining the use of various approaches, considering wind power system as a complex technology. The approach to the business system is used as an analytical framework, focusing on efficiency, effectiveness and criteria for development.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.156
GPT teacher head0.357
Teacher spread0.201 · 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 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

Citations0
Published2011
Admission routes1
Has abstractyes

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