Bibliographic record
Abstract
Several methods have been developed through the years to mount lenses with the ultimate goal of minimizing their positioning errors with respect to the nominal optical layout. This is a non-trivial task, since it requires either very well controlled manufacturing tolerances or alignment of the optical components into their mounts. This paper reviews the classical lens mounting methods and introduces new solutions to improve the centering accuracy. First, an improved drop-in method called auto-centering is described. This method is based on the use of geometrical relationship between the lens diameter, the lens radius of curvature, and the thread angle of the retaining ring to provide centring error typically less than 0.5 arcmin. In addition, an innovative method that relies on geometric principles to auto-center optomechanical parts to each other is described. The method allows to auto-center an optical group in a main barrel, to perform an axial adjustment of an optical group inside a main barrel, and to perform stacking of multiple barrels within 5 µm of centering error. Finally, to improve the centering accuracy of common method used to center injection molded plastic lenses, a new concept using toroidal interfaces has been developed. This method allows a reduction by at least a factor of two the lens centering error compared to methods based on radial clearance fit for the same manufacturing tolerances.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 0.006 |
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".