Quantifying the Effect of Spectacle Frame Dimensions on Wind-Induced Ocular Plane Evaporation Using an in Vitro Model
Bibliographic record
Abstract
PURPOSE: To quantify the effect of spectacle frame dimensions on wind-induced ocular plane evaporation. METHODS: A drop of 0.5 μL water was pipetted onto an eye of a mannequin head. The face was fitted with a spectacle frame. A fan positioned 10 cm away directed air (185 CFM) toward the face and the time required for the drop to evaporate was recorded. This procedure was repeated with 31 different frames to obtain evaporation times for various eye sizes, vertical heights, vertex distances, temperature, and humidity. This was also repeated 30 times without spectacle wear to obtain evaporation times for various temperature and humidity conditions. RESULTS: Spectacle wear increased evaporation times compared with nonspectacle wear, in both high (>35%) and low humidity (<30%) conditions (both P<0.01). Humidity was correlated with evaporation time, regardless of spectacle and nonspectacle wear (both P<0.01). Evaporation time did not correlate with spectacle eye size, vertical height, or vertex distance (all P≥0.21). CONCLUSION: This study showed that spectacle wear guarded against wind-induced evaporation at the ocular plane compared with nonspectacle wear. However, once spectacles were worn, eye size, vertical height, and vertex distance were not correlated with evaporation times. Humidity drove evaporation independent of spectacle wear.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.002 | 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".