Mixed reality for laser safety at advanced optics laboratories
Bibliographic record
Abstract
Nowadays, high-power and multi-spectral lasers are increasingly demanded in many scientific experiments and industrial processes. However, direct exposure to these laser sources can lead to permanent eye damage. Individuals conducting research and development with these laser sources are required to wear laser safety goggles as a form of eye protection. Currently, most laser safety goggles are based on optical spectral filters, which could filter up to 99 % of the visible spectrum, rendering researchers working in hazardous and complex laboratory environments visually impaired. In this work, we present a novel laser eye protection method that could provide full-band laser protection without reducing the user's visibility of the environment. We demonstrate a system based on a virtual reality (VR) headset with a stereoscopic see-through camera, which could be constructed in a way that all laser and ambient light is blocked from the human eyes. We revisit an empirical evaluation of our prototype conducted at the laser science and technology group (FS-LA) at DESY with 18 participants, including 14 laser experts. The evaluation results reveal that MR technology has the potential to offer a safe alternative to conventional laser safety goggles and has tremendous prospects in improving the complex and hazardous working conditions at advanced optics laboratories.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".