MétaCan
Menu
Back to cohort
Record W2901152455 · doi:10.25071/10315/35397

Evaluation Of Energy Efficient Propulsion Technologies For Unmanned Aerial Vehicles

2018· article· en· W2901152455 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

VenueProgress in Canadian Mechanical Engineering · 2018
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Technologies Research
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsPropulsionAerospace engineeringAeronauticsRemotely operated underwater vehicleComputer scienceAutomotive engineeringEnvironmental scienceEngineeringMobile robotRobotArtificial intelligence

Abstract

fetched live from OpenAlex

The transition to cleaner, more efficient and longerendurance aircraft is at the forefront of current research and development in air transportation systems. The focus of this research is to experimentally evaluate Hybrid Propulsion and Energy Harvesting Systems in Unmanned Aerial Vehicles (UAV). Hybrid systems offer several potential benefits over more conventional gasoline and electric systems including lower environmental impacts, reduced fuel consumption, longer endurance, redundancy and distributed propulsion. Additional energy efficiency can be achieved by harvesting some of the thermal energy of the exhaust gases. By using the Seebeck effect, the temperature gradient between ambient air and the exhaust can be used to generate electric power, making it possible to eliminate costly mechanical systems such as alternators and reduce fuel consumption.

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)0.001
metaresearch head score (Gemma)0.001
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: Empirical
Teacher disagreement score0.772
Threshold uncertainty score0.686

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.021
GPT teacher head0.285
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