MétaCan
Menu
Back to cohort
Record W2160777885 · doi:10.1109/tec.2006.889603

Harnessing High-Altitude Wind Power

2007· article· en· W2160777885 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIEEE Transactions on Energy Conversion · 2007
Typearticle
Languageen
FieldEngineering
TopicAerospace Engineering and Energy Systems
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsAerospace engineeringWind powerAerodynamicsElectricity generationElectricityMeteorologyScale (ratio)Environmental scienceElectrical engineeringAltitude (triangle)Power (physics)Computer scienceAutomotive engineeringEngineeringPhysicsMathematics

Abstract

fetched live from OpenAlex

Flying electric generators (FEGs) are proposed to harness kinetic energy in the powerful, persistent high-altitude winds. Average power density can be as high as 20 kW/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> in an approximately 1000-km-wide band around latitude 30deg in both the hemispheres of the Earth. At 15 000 ft (4600 m) and above, tethered rotorcraft, with four or more rotors mounted on each unit, could give individual rated outputs of up to 40 MW. These aircrafts would be highly controllable and could be flown in arrays, making them a large-scale source of reliable wind power. The aerodynamics, electrics, and control of these craft are described in detail, along with a description of the tether mechanics. A 240 kW craft has been designed to demonstrate the concept at altitude. It is anticipated that large-scale units would make low-cost electricity available for grid supply, for hydrogen production, or for hydro-storage from large-scale generating facilities

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.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.890
Threshold uncertainty score0.947

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 head0.005
GPT teacher head0.183
Teacher spread0.177 · 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