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Record W4353086038 · doi:10.54097/hset.v38i.5995

Designing And Modeling Of De Havilland Canada Dash 8 Q300 Base on Simpleplanes

2023· article· en· W4353086038 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

VenueHighlights in Science Engineering and Technology · 2023
Typearticle
Languageen
FieldEngineering
TopicSimulation and Modeling Applications
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsComputer scienceDashSoftwareSoftware engineering3D modelingVisual modelingBase (topology)Computer graphics (images)Engineering drawingIndustrial engineeringSystems engineeringSimulationEngineeringUnified Modeling LanguageProgramming languageMathematicsOperating system

Abstract

fetched live from OpenAlex

Mathematics development is aided by image-based 3D modeling. In certain ways, visual 3D modeling should be considered a branch and application of mathematical morphology. Airplanes are sometimes required in the scene due to storyline requirements. Due to the complexity and time-consuming characteristics of traditional modeling methods, this paper put forward a model making scheme using SimplePlanes software was proposed. After development and testing based on SimplePlanes software, the model is similar to the real aircraft, and using SimplePlanes will save a lot of time and meet the use requirements. In this paper, the method of modeling De Havilland Canada Dash 8 Q300 using SimplePlanes is explained in detail together with the basic modeling logic of this software. A user study is performed to demonstrates the overall good quality of the result and a significant improvement in terms of time efficiency.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.295

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.014
GPT teacher head0.226
Teacher spread0.212 · 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