Optomechanical mounts for high stability in harsh environmental conditions
Bibliographic record
Abstract
The development of a new optical device often faces the same challenges, more specifically at the concept validation level where their development risks are very high. It commonly leads to a laboratory proof-of-concept to test the principle usually built with commercially available off-the-shelf components with high degree of adjustments. The level of robustness, the compactness, and the portability of the device are limited by these adjustable mounts. A breadboard prototype is then developed integrating more custom mounts, but it may require substantial optomechanical effort to converge on an improved version. QuickPOZ, a new generation of mounts, has been developed to fill the gap between the concept idea and the first prototype runs. These standard mounts and breadboards are an easy way to build optical breadboards quickly and accurately robust. They can be used in the development process as soon as the proof-of-concept validation, and up to small run prototyping to test the market. These mounts combine the QuickCTR-edge technology to self-center optics and their mounts, with a high robustness level. QuickPOZ mount’s optical performance results are presented and discussed over a wide operating temperature range between -40°C up to 50°C.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.009 | 0.003 |
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".