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Record W2900762691 · doi:10.25071/10315/35286

Motion Of Pigments In Thin Paint Films

2018· article· en· W2900762691 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.

Bibliographic record

VenueProgress in Canadian Mechanical Engineering · 2018
Typearticle
Languageen
FieldPhysics and Astronomy
TopicColor Science and Applications
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMotion (physics)Materials sciencePigmentComputer scienceComputer graphics (images)Computer visionArtVisual arts

Abstract

fetched live from OpenAlex

When thin paint layers are exposed to heat, evaporation of the solvent begins, and significant movement within the paint film occur, causing temperature and concentration gradients. A clear coat paint was used, which had a density of 988 kg/m 3 , viscosity of 240 cP and a surface tension of 26 mN/m and was mixed with spherical solid blue particles with a mean diameter 26 m were added to the paint at 2% concentration of the total mass. A knife edge was used to coat stainless steel substrates at different thicknesses and placed in a convection oven at 121 o C for 30 minutes. Images of the samples during the curing process were taken, showing how the pigments moved from low to high surface tension areas. Colour measurements were taken to show that increasing the thickness of the same paint significantly decreases the lightness value.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.906
Threshold uncertainty score0.997

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 head0.008
GPT teacher head0.244
Teacher spread0.236 · 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