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Record W2978001853 · doi:10.55411/26652544.103

Estimación de las características de rendimiento de un rotor eólico biomimético para aplicaciones de bombeo

2018· article· es· W2978001853 on OpenAlexaffabout
David Castañeda, Fabio E. Sierra, C. Guerrero

Bibliographic record

VenueLetras ConCiencia TecnoLógica · 2018
Typearticle
Languagees
FieldEngineering
TopicWind Energy Research and Development
Canadian institutionsWind Energy Institute of Canada
Fundersnot available
KeywordsHumanitiesRotor (electric)PhysicsArt

Abstract

fetched live from OpenAlex

Los rotores bioinspirados para sistemas eólicos han sido enfocados para generación de electricidad, por ejemplo los basados en la semilla de sicomoro (Cranfield University Media Centre;) y las aletas propulsoras de la ballena azul (Wind Energy Institute of Canada, WEIC). En contraste, se desconocen rotores bioinspirados para sistemas eólicos de bombeo. En Colombia existen estudios de las Universidades Nacional y Los Andes relacionados con aplicaciones de energía eólica para bombeo, los cuales apuntan a optimizaciones y adaptaciones de componentes distintos al rotor.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.016
GPT teacher head0.281
Teacher spread0.265 · 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 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
Published2018
Admission routes2
Has abstractyes

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