Bibliographic record
Abstract
PURPOSE: The purposes of this study were to find the problems of afocal iseikonic lenses as used for near vision and to elaborate the principles and designs of the ideal near iseikonic lenses. METHODS: By analyzing the image-object relationship and imaging processes, the formulae were derived to quantitatively and qualitatively describe two errors of afocal iseikonic lenses as used for near vision: 1) inequality of accommodative demands to both eyes and 2) the difference between the actual and the nominal magnification. Formulae were derived to calculate and design the ideal near iseikonic lenses. RESULTS: For iseikonic lenses now used for near vision, inequality of accommodative demands to both eyes and difference between the actual and nominal magnification increased as the magnification increased and the viewing distance decreased. Afocal iseikonic lenses have significant inequality of accommodative demands to both eyes with 3% magnification at a viewing distance of 25 cm or with 5% magnification at 40 cm. The ideal near iseikonic lenses were found to be flatter than the afocal ones. A series of the ideal near iseikonic lenses were designed with some practical considerations. The central thickness and the refractive index of the lenses affect magnification and lens shape. The thicker the lenses, the higher the magnification. The higher refractive index also gives higher magnification. CONCLUSIONS: Iseikonic lens design is much different for distance and near vision. When the iseikonic lenses are used at a distance other than the designated distance, they will cause blurred vision and artificial aniseikonia as a result of inequality of accommodative demands to both eyes. In research as well as clinical settings, it is necessary to apply the ideal near iseikonic lenses for near vision.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".