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Record W2060709468 · doi:10.4271/2013-01-2176

Shear Driven Droplet Shedding on Surfaces with Various Wettabilities

2013· article· en· W2060709468 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueSAE International Journal of Aerospace · 2013
Typearticle
Languageen
FieldMaterials Science
TopicSurface Modification and Superhydrophobicity
Canadian institutionsConcordia University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsShear (geology)MechanicsNanotechnologyAerospace engineeringPhysicsMaterials scienceEngineeringComposite material

Abstract

fetched live from OpenAlex

<div class="section abstract"><div class="htmlview paragraph">Experimental study is performed to analyze the shedding behavior of droplets with different shear flow speeds typical of those in the flight conditions. Droplet shedding phenomena has significant effect on ice accumulation on critical components such as airfoil and nacelle. In order to mimic this scenario experimental set up is designed to generate shear flow as high as 90m/s. The high shear effect is combined to the surface wettability impact by using hydrophilic and superhydrophobic surfaces. It is shown that the wetting length of the droplet on hydrophilic surface increases by shear speed while on the superhydrophobic surface a drastic reduction on wetting length is detected. Furthermore, it is observed that the droplet is detached from the superhydrophobic surface with moderate shear speeds.</div></div>

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 categoriesInsufficient payload (model declined to judge)
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 score0.259
Threshold uncertainty score0.998

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 communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.012
GPT teacher head0.245
Teacher spread0.233 · 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