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Record W4210443063 · doi:10.1049/pbpo169e_ch12

Development and experience with a vertical-axis hydrokinetic power generation system

2021· book-chapter· en· W4210443063 on OpenAlexaff
Clayton Bear, Michael Bear

Bibliographic record

VenueInstitution of Engineering and Technology eBooks · 2021
Typebook-chapter
Languageen
FieldEngineering
TopicWind Energy Research and Development
Canadian institutionsAlberta Energy
Fundersnot available
KeywordsOceanographyMarine engineeringComputer scienceGeographyEngineeringEnvironmental scienceGeology

Abstract

fetched live from OpenAlex

Chapter Contents: 12.1 Introduction 12.2 History 12.3 Technology 12.4 Applications 12.5 System configuration 12.6 Development program 12.6.1 Early days 12.6.2 Novel approach 12.7 Initial prototypes 12.7.1 Pointe du Bois test program 12.8 First-generation designs 12.8.1 New bearing concept 12.9 Second-generation designs 12.9.1 Impact on aquatic life 12.10 Tidal deployments 12.11 Summary 12.12 Case studies 12.12.1 Ringmo, Nepal 12.12.2 Hpa-An, Myanmar References

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.014
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0140.004

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 head0.010
GPT teacher head0.179
Teacher spread0.169 · 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 designBench or experimental
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
Published2021
Admission routes1
Has abstractyes

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