CRITERIA FOR EVALUATING THE ACCURACY OF SURFACE TENSION VALUES FROM DIGITAL VISION SYSTEMS
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.
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.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it