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Record W6948026789 · doi:10.4224/40002981

Guidelines for simulating hydrokinetic turbine arrays

2023· report· en· W6948026789 on OpenAlexaffvenue

Bibliographic record

VenueNPARC · 2023
Typereport
Languageen
FieldEnvironmental Science
TopicSpecies Distribution and Climate Change
Canadian institutionsUniversité LavalNational Research Council CanadaMinistère des Ressources naturelles et des Forêts (Québec)Natural Resources CanadaInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsConsistency (knowledge bases)HydropowerResource (disambiguation)TurbineMains electricityElectricityExtraction (chemistry)

Abstract

fetched live from OpenAlex

The extraction of energy from flowing water in rivers and tidal regions is gaining acceptance around the world as a means of generating electricity without the use of conventional hydropower dams. The purpose of this guideline is to provide a uniform methodology that will ensure consistency and accuracy in the estimation, measurement, characterisation, and analysis of the velocity/hydrokinetic resource at sites that could be suitable for the installation of an individual or array of turbines, together with defining a standardised methodology with which this resource can be described and reported.

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)0.002
metaresearch head score (Gemma)0.006
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: Methods · Consensus signal: Methods
Teacher disagreement score0.029
Threshold uncertainty score0.096

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0040.001
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0290.014

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 head0.238
GPT teacher head0.389
Teacher spread0.152 · 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
GenreMethods

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
Published2023
Admission routes2
Has abstractyes

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