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Record W4317583899 · doi:10.2514/6.2023-0806

3D measurement of ice crystal accretion using a plenoptic camera

2023· article· en· W4317583899 on OpenAlexaboutno aff
Martin Eberhart, Stefan Loehle, Felix Grigat, Jonathan Connolly, Matthew McGilvray, David Gillespie

Bibliographic record

VenueAIAA SCITECH 2023 Forum · 2023
Typearticle
Languageen
FieldEngineering
TopicIcing and De-icing Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsIcingIce crystalsWind tunnelAccretion (finance)Remote sensingImage resolutionLaserGeologyEnvironmental scienceMaterials scienceOpticsMeteorologyPhysicsAerospace engineeringEngineeringAstrophysics

Abstract

fetched live from OpenAlex

View Video Presentation: https://doi.org/10.2514/6.2023-0806.vid Monitoring the formation of ice layers on airfoils in three dimensions during experiments in an icing wind tunnel is extremely challenging. For the first time, this paper proposes the use of a single plenoptic camera to perform transient, non-intrusive measurements of ice crystal accretion. First experiments have been conducted in the Altitude Icing Wind Tunnel (AIWT) at the National Research Center (NRC) of Canada under different icing conditions. Results show the evolution of the 3D shape of the accreted ice in high spatial and temporal resolution. Post-test shapes are compared to measurements using an established laser scanning device. The results of the two methods are in excellent agreement and show a mean deviation of the plenoptic camera of about 0.1 mm.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.031
GPT teacher head0.241
Teacher spread0.210 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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Same venueAIAA SCITECH 2023 ForumSame topicIcing and De-icing TechnologiesFrench-language works237,207