Introducing a graphical user interface for dynamic contact angle determination
Bibliographic record
Abstract
Contact angle goniometry is an important characterization technique that can determine crucial information, such as the wettability, interfacial tension, and adhesion properties of solid and liquid surfaces alike. However, while this technique is already widespread, the by-hand analysis process of elucidating the advancing and receding contact angles (ARCAs) from the actual data set has many pitfalls and is fraught with human error. In this article, we introduce a graphical user interface (GUI) called ARCA Finder that drastically simplifies the analysis process by displaying the contact angle data in a novel perspective and aiding the user to determine the most accurate measurement from the available data based on the full definition of the dynamic contact angles. The goal of this invention is to improve measurement accuracy by reducing human error in goniometry, while also improving the repeatability of measurements among different researchers. By testing this approach alongside by-hand analysis on both synthetic and real dynamic contact angle videos, our results demonstrate a noticeable difference in the measured values, which suggests that the ARCA Finder GUI improves the measurement accuracy compared to the standard approach.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.061 | 0.019 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".