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Record W2125056769 · doi:10.1002/pip.2466

A review of floating photovoltaic installations: 2007–2013

2014· review· en· W2125056769 on OpenAlexaff
Kim Trapani, Miguel Redón Santafé

Bibliographic record

VenueProgress in Photovoltaics Research and Applications · 2014
Typereview
Languageen
FieldEngineering
TopicWave and Wind Energy Systems
Canadian institutionsCentre for Excellence in Mining Innovation
FundersUniversitat de ValènciaUniversitat Politècnica de ValènciaNational Institute of Advanced Industrial Science and Technology
KeywordsPhotovoltaic systemEnvironmental scienceMarine engineeringScale (ratio)AgricultureAgricultural engineeringCivil engineeringMeteorologyEngineeringEcologyGeographyElectrical engineeringCartographyBiology

Abstract

fetched live from OpenAlex

Abstract The paper gives a review of the various projects that have been realised in throughout the years. These have all been in enclosed water bodies such as reservoirs, ponds and small lakes. The main motivation for the floating photovoltaic (PV) panels was the land premium, especially for agricultural sites were the land was more valuable for growth of the crops (in these cases, grapes because the sites were wineries). The PV panels of the existing projects are mounted on a rigid pontoon structure and vary between horizontal and tilted installations. Future concepts proposed for marine and large lacusterine sites are envisaged to incorporate laminated thin film PV, which would allow the structure to be flexible and able to yield with the oncoming waves, and submergible arrays, which would be submerged in harsh weather conditions. Interest and research has been developing in this niche field throughout the years and has currently reached the megawatt scale with even bigger plans for the future. Copyright © 2014 John Wiley & Sons, Ltd.

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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.007
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.006
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.003

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 head0.076
GPT teacher head0.392
Teacher spread0.316 · 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
GenreReview

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

Citations363
Published2014
Admission routes1
Has abstractyes

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