Bibliographic record
Abstract
High average power lasers, hundreds of watts to kilowatts, are common in industrial environments where the beams are used to cut, weld, or engrave high melting point materials such as metals. In the research environment high peak power lasers, from gigawatts to terawatts, are common for pulsed lasers (ns, ps and fs) used in ablation and ultrafast spectroscopy. However, due to the low pulse repetition frequencies, those lasers normally have intermediate average powers in the order of a few watts. In the last few years, groups at the University of Toronto have started requiring high-average power lasers for their projects. These lasers’ powers range from a few hundred watts to one kilowatt. Normally these types of lasers are completely enclosed and certified as “class I working environment” by the laser's manufacturer, but the particular needs of some projects they are used on require open beam lasers. In this paper, we'll focus on two lasers, one used for welding in a machine shop environment and the other in a research group developing a novel metal 3D printer. We will discuss the challenges of controlling different types of hazards, such as high-power beams, respiratory hazards connected to metal powder, Oxygen displacement by inert gases, etc. Changes in the laser safety training designed for the operators (craftspeople), who don't have much experience working with lasers and have little knowledge about physics will be also presented.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.055 | 0.018 |
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".