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Record W4392383245 · doi:10.32920/25336327

Prediction Model Validation for Circular Flight Paths for CREATeV

2024· preprint· en· W4392383245 on OpenAlex
Lydia Habib

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

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicAdvanced Aircraft Design and Technologies
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsFlight testPower (physics)Position (finance)Renewable energyAerospace engineeringHeading (navigation)SimulationComputer scienceEngineeringElectrical engineeringPhysics

Abstract

fetched live from OpenAlex

<p>This study focused on the analysis of flight test data collected during an 18-hour flight for CREATeV. CREATeV is an unmanned solar Clean Renewable Energy Aerial Test Vehicle that aims to achieve complete ultra-long endurance flight using 96 wing-mounted solar cells. Flight test data were extracted to understand the aircraft’s power collection and usage. A prediction model was validated by considering wind effects and position of the sun throughout a selected flight segment. The model was validated through methods discussed in this study. The validated prediction model can be applied to any circular flight to predict the power required for banking turn, solar power collected, battery charging power, aircraft bank angle and heading. A theoretical flight scenario was inputted into the validation model to demonstrate how the model may be used.</p>

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: Methods · Consensus signal: none
Teacher disagreement score0.768
Threshold uncertainty score0.774

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 science0.0000.001
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 head0.035
GPT teacher head0.262
Teacher spread0.227 · 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