Dynamics of an Artificial Tear Film on Contact Lenses in Response to a Moving Force Mimicking Fingertip Application
Bibliographic record
Abstract
Globally, it is estimated that more than 140 million people wear contact lenses. As a result, users demonstrate varying levels of dexterity and consistency when inserting lenses, often requiring fine adjustments. Moreover, the diverse structure and quality of individuals' precorneal tear films lead to different tear film responses under various insertion conditions. In this work, we mimic such tear film dynamics using a force probe coupled with reflection interference contrast microscopy. We use an in-house prepared artificial tear film solution that mimics human tear film composition deposited on commercially available soft contact lenses. The probe is used to replicate contact forces that resemble the pressure and motion of a human fingertip during lens insertion. Our findings reveal that contact-induced deformations and resulting film morphologies are sensitive to the volume, i.e., thickness of the tear film: from localized wetting ridges with memory effects for thicker films to traveling deformations with permanent wrinkling for thinner films. In extremely thin films, we observe that film evaporation dominates over contact-driven dynamics, although mobile contact forces can locally reverse rupture spots in the tear film.
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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 | 0.000 | 0.000 |
| 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) | 0.001 | 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 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".