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Record W3156786519 · doi:10.1097/icl.0000000000000783

Quantifying the Effect of Spectacle Frame Dimensions on Wind-Induced Ocular Plane Evaporation Using an in Vitro Model

2021· article· en· W3156786519 on OpenAlexaff
Cassandra B. Huynh, William Ngo

Bibliographic record

VenueEye & Contact Lens Science & Clinical Practice · 2021
Typearticle
Languageen
FieldMedicine
TopicOcular Surface and Contact Lens
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsSpectacleEvaporationHumidityMaterials scienceOpticsVertex (graph theory)PhysicsMeteorologyMathematicsGraph

Abstract

fetched live from OpenAlex

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.

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.015
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.365
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.132
GPT teacher head0.444
Teacher spread0.312 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2021
Admission routes1
Has abstractyes

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