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Record W1142916984 · doi:10.1177/0954406215590186

Shear-driven droplet coalescence and rivulet formation

2015· article· en· W1142916984 on OpenAlex
Sara Moghtadernejad, Mohsen Jadidi, Nabil Esmail, Ali Dolatabadi

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

VenueProceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science · 2015
Typearticle
Languageen
FieldEngineering
TopicIcing and De-icing Technologies
Canadian institutionsConcordia University
Fundersnot available
KeywordsIcingCoalescence (physics)Shear (geology)MechanicsAerodynamicsMaterials scienceAerospace engineeringMeteorologyPhysicsEngineeringComposite materialAstrobiology

Abstract

fetched live from OpenAlex

Icing on aerodynamic surfaces occurs due to the accumulation of rain droplets when the surrounding temperature is below the freezing temperature. It is well known that icing phenomenon alters the aircraft aerodynamic forces and may cause serious damage. Therefore, studying water droplet behavior, such as shedding and coalescence serves as the primary step which can lead to understanding the fundamental physics of aircraft icing. Hence, in this study an experimental approach is used to investigate the shear-driven droplet shedding and coalescence on a hydrophilic substrate which can serve as the building block for the formation of rivulets.

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)0.002
metaresearch head score (Gemma)0.002
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.345
Threshold uncertainty score0.621

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.018
GPT teacher head0.218
Teacher spread0.201 · 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