Compensating for Vertical Anisometropic Imbalance by the Positioning of Segment Centers
Bibliographic record
Abstract
Prismatic imbalance produced in the reading area by an anisometropic spectacle correction can be offset by various methods. If a reading addition is present, one method is to provide differently shaped segments and allowing their optical centers to be separated vertically while the segment tops remain in horizontal alignment. Traditionally, to apply this method, the relative prismatic difference at the reading point is first determined by a thin lens application of Prentice's rule. This rule is then applied a second time to determine the placement of the segments that will offset the prismatic difference produced by the major lenses. However, the conventional application of Prentice's rule for determining the fusional demand in a particular area of the spectacle field often produces very large errors, which will affect the calculation of the placement of the segments. This article develops and demonstrates more accurate methods for applying the principle of compensating segments.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".