CRITERIA FOR EVALUATING THE ACCURACY OF SURFACE TENSION VALUES FROM DIGITAL VISION SYSTEMS
Bibliographic record
Abstract
Experimental techniques for measuring surface tension using the shape of either axisymmetric sessile or pendant drops have existed for many years. Recent developments in digital image acquisition and processing have permitted the computerization of the process, by which the coordinates of the drop’s edge profile are obtained. Algorithms like the axisymmetric drop shape analysis–profile (ADSA–P) program use the edge profile coordinates to estimate quantities such as the surface tension, drop volume, and contact angle. The precision of these estimated quantities depends on various effects that influence the accuracy by which the edge profile coordinates are acquired. We have modeled this uncertainty in coordinate information as a perturbation effect and related the size of the perturbation to the surface tension accuracy. Two analogous relations were used to set regions of surface tension accuracy, e.g., or as functions of the magnification of the drop, CCD camera array size, pixel size, drop shape, and drop edge precision. An algorithm for the design of various vision systems based on these criteria will be discussed and illustrated.
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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 | 0.019 | 0.192 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
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".