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Record W4391024873 · doi:10.15353/cjo.v48i4.4510

Compression Testing of Three Soft Lens Polymers with a Simulated Fingernail

2021· article· en· W4391024873 on OpenAlex

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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueCanadian journal of optometry/CJO. Canadian journal of optometry · 2021
Typearticle
Languageen
FieldEngineering
TopicTribology and Lubrication Engineering
Canadian institutionsnot available
Fundersnot available
KeywordsLens (geology)Compression (physics)Compressive strengthPolymerMaterials scienceComposite materialOpticsPhysics

Abstract

fetched live from OpenAlex

An analysis of the mechanical proper­ties of finished lenses utilizing com­pression testing identifies factors contributing to soft lens damage. Sauflon 70, Snoflex 50, and Toyo 515 PolyHEMA lenses, all of plano power and equal thicknesses were com­pressed within the optical zone by loads exerted by a simulated fingernail made of guitar pick material. Six lenses of each polymer were used for each of three tests. It was found that Toyo 515 PolyHEMA had a relatively lower compressive strength than the other two non-HEMA materials; that the Sauflon 70 had the least ability to recover once compressed; and that all three polymers did not appear to recover their previous compression strength after undergoing a dehydra­tion/rehydration cycle. In the case of the Toyo 515 lenses, this last result was confirmed statistically.

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 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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.232
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0040.004
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.016
GPT teacher head0.244
Teacher spread0.228 · 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