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Record W2043641540 · doi:10.1115/omae2013-11368

MARINET: The Research Infrastructure Network Gaining International Support and Accelerating the Development of Marine Renewable Energy

2013· article· en· W2043641540 on OpenAlexaboutno aff
Mark G. Healy, Raymond Alcorn, Tony Lewis

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnvironmental Science
TopicMaritime Transport Emissions and Efficiency
Canadian institutionsnot available
Fundersnot available
KeywordsRenewable energySoftware deploymentBusinessOffshore wind powerProcess (computing)Wind powerMarine energyTelecommunicationsEngineeringEnvironmental economicsComputer scienceElectrical engineeringEconomics

Abstract

fetched live from OpenAlex

MARINET (Marine Renewables Infrastructure Network) is an EC-funded marine renewable energy infrastructure initiative which seeks to accelerate the development and commercial deployment of marine renewable energy technologies — wave, tidal & offshore-wind — by streamlining the testing process. World-class research centers and organizations are coming together in a network to offer periods of free-of-charge access to their world-class testing facilities and to develop a joint approach to testing standards, testing research and industry training & networking. The growing network, with more than 30 full and associate partner research centers, has over 40 specialist marine research facilities covering all scales from laboratory to sea. It is spread across 11 EU countries and international partner countries such as Brazil, Taiwan, Canada and the US. This paper outlines what MARINET is, what it has already achieved at the half-way point in the initiative, where it is going and who can benefit now and in the future.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.688
Threshold uncertainty score0.955

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0460.000

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 head0.024
GPT teacher head0.257
Teacher spread0.233 · 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.

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

Citations1
Published2013
Admission routes1
Has abstractyes

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