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 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) | 0.005 | 0.001 |
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 teacher head, 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".