MétaCan
Menu
Back to cohort
Record W2558693562 · doi:10.1109/lra.2016.2633623

Design of a Passive Vertical Takeoff and Landing Aquatic UAV

2016· article· en· W2558693562 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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueIEEE Robotics and Automation Letters · 2016
Typearticle
Languageen
FieldEngineering
TopicUnderwater Vehicles and Communication Systems
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsTakeoffTakeoff and landingWingMarine engineeringAerospace engineeringAeronauticsFlight control surfacesSimulationEngineeringEnvironmental scienceComputer scienceAutomotive engineeringAerodynamics

Abstract

fetched live from OpenAlex

With the goal of extending unmanned aerial vehicles mission duration, a solar recharge strategy is envisioned with lakes as preferred charging and standby areas. The Sherbrooke University Water-Air VEhicle (SUWAVE) concept developed is able to takeoff and land vertically on water. The physical prototype consists of a wing coupled to a rotating center body that minimizes the added components with a passive takeoff maneuver. A dynamic model of takeoff, validated with experimental results, serves as a design tool. The landing is executed by diving, without requiring complex control or wing folding. Structural integrity of the wing is confirmed by investigating the accelerations at impact. A predictive model is developed for various impact velocities. The final prototype has executed multiple repeatable takeoffs and has succeeded in completing full operation cycles of flying, diving, floating, and taking off.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.918
Threshold uncertainty score0.196

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 head0.019
GPT teacher head0.208
Teacher spread0.188 · 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