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Record W3198774900 · doi:10.1680/jsuin.21.00028

New ellipse-fitting method for contact angle measurement

2021· article· en· W3198774900 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

VenueSurface Innovations · 2021
Typearticle
Languageen
FieldEngineering
TopicElectrowetting and Microfluidic Technologies
Canadian institutionsYork University
Fundersnot available
KeywordsEllipseContact angleDrop (telecommunication)Rotational symmetryMaterials scienceMeasure (data warehouse)Sessile drop techniqueWork (physics)OpticsCurve fittingGeometryMathematicsComputer sciencePhysicsComposite materialEngineeringThermodynamicsMechanical engineeringStatistics

Abstract

fetched live from OpenAlex

In this work, the authors examined the commonly used fitting methods for optical contact angle measurement. It was shown that currently, there is no available fitting method that can measure the contact angle of a non-axisymmetric drop without a clear view of the drop baseline. This work contributes to contact angle measurement methods by providing a new ellipse-fitting method. The new method fits the drop profile with a general equation of an ellipse whose major and minor axes are not necessarily parallel and normal with the substrate, respectively. The advantage of this method is that it can measure the contact angle of a non-axisymmetric drop without a clear view of the drop baseline. The measurement results were validated by comparing against the DropSnake and polynomial fitting methods. Generally good agreement was seen, which is important as it validates this method as the only available method for finding the contact angle of a droplet when the contact line is not visible – for example, in a condensation environment where many drops can block the view of each other’s contact line.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.479
Threshold uncertainty score0.497

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.031
GPT teacher head0.270
Teacher spread0.239 · 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