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Record W4415679765 · doi:10.1016/j.renene.2025.124668

Tidal and wave driven hydrokinetic power in atolls of an amphidromic region in the Pacific Ocean

2025· article· en· W4415679765 on OpenAlexfundno aff
Federico Zilic de Arcos, Franck Lucas, Marc Lafosse, Grégory Pinon

Bibliographic record

VenueRenewable Energy · 2025
Typearticle
Languageen
FieldEngineering
TopicWind Energy Research and Development
Canadian institutionsnot available
FundersCanadian Nautical Research SocietyH2020 Marie Skłodowska-Curie ActionsHorizon 2020 Framework ProgrammeConseil Régional de Haute Normandie
KeywordsAtollTidal powerCurrent (fluid)Kinetic energyFlow (mathematics)Energy fluxTidal currentFlux (metallurgy)Flood myth

Abstract

fetched live from OpenAlex

French Polynesia is located in an amphidromic area of the Pacific Ocean, a region of low tidal ranges. Atolls, ring-shaped islands with internal lagoons that are common to this region, often show strong currents in channels that connect the lagoon to the ocean. This study shows long-term in-situ flow measurements using acoustic-doppler current profilers that were deployed in the main channels of Manihi and Takaroa, two atolls of French Polynesia, to explore their kinetic energy potential. Despite the lack of significant tidal effects on sea levels, flow measurements show strong currents with a semidiurnal behaviour dominated by the lunar constituent. An asymmetry in the ebb and flood currents is observed and correlation with wave climate indicate that the atolls operate as large wave overtopping devices, increasing the kinetic energy flux through the channel, and extending the ebb flow period. An energetic assessment shows a potential for tidal stream turbines to produce a substantial proportion of the energy demands of Manihi and Takaroa. This energy production would likely have a limited environmental and visual impact, and would allow for a reduction in carbon emissions and reliance on fossil fuels.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.380
Threshold uncertainty score0.359

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.007
GPT teacher head0.196
Teacher spread0.189 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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
Published2025
Admission routes1
Has abstractyes

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