Bibliographic record
Abstract
This paper begins with a review of significant laser pointer news since ILSC 2017. These include new laws in the U.K., Canada and Switzerland; an MIT-developed laser pointer detection system, the SAE-published ARP6378 with pilot mitigation recommendations, a review of 111 laser pointer eye injuries worldwide, the status of FDA’s 2016 proposal to allow only red laser pointers, and the new LaserIncidents.com website that lists known databases that compile laser incidents and accidents. The paper then looks at methods for reducing the number and severity of laser pointer incidents. For example, Australia and New Zealand have laws severely restricting ownership of laser pointers over 1 mW. In Australia, aircraft illumination incidents increased significantly after the 2008 ban and currently are roughly equal to U.S. incidents on a per capita basis. In New Zealand, aircraft incidents increased after a ban went into effect in 2014. The ARP6378 document cites pilots as the last line of defense. Pilot education, training and protective eyewear/windscreens are discussed in the document. Changes in labeling are suggested. The usefulness of prosecuting laser offenders is discussed. A summary is given of a Jan. 2019 symposium in Tokyo, seeking new laws and ideas for reducing aircraft incidents, consumer eye injuries, and injuries from laser cosmetic devices. Finally, suggested directions for future research are given.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.005 |
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".