Spatial dependence of surface error slopes on tolerancing panoramic lenses
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.
Bibliographic record
Abstract
Surface irregularity errors are conventionally used to specify fabrication accuracy of spherical, aspheric, or plane surfaces. However, in some cases, the amplitude of the irregularities fails to fully describe the surface accuracy requirement when the pupil size is small compared to the surface diameter. In such cases, the irregularity slope will induce distortion. A spatially dependent representation of the irregularity slope is proposed and implemented to specify the surface accuracy. As an optical design example, we study in detail the case of the front surfaces of a fish-eye lens and a panomorph lens. Panoramic lenses are characterized by a small entrance pupil and by important distortion. For both lenses, we found that the novel field-dependent mathematical descriptor provided a nearly perfect agreement with Monte Carlo analyses and can be used to specify the spatially dependent irregularity requirement. The approach is not limited to wide-angle lenses.
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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 | 0.000 |
| 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) | 0.000 | 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 it