Education and Collaboration to Manage the Risks of High Energy Materials in Research and Development
Bibliographic record
Abstract
Hazard recognition is crucial for mitigating risks in research laboratory settings where new chemicals and processes are introduced frequently. Where hazards are familiar to the researcher, successful mitigation strategies are likely similarly well-known. While some high energy materials (i.e., chemicals that decompose rapidly with significant energy release) are easily recognized even by nonexperts, the exploratory nature of chemical research can result in the inadvertent formation of high energy materials both known and unknown, putting researchers and their science at risk of a safety incident. In this paper, we discuss approaches at The Dow Chemical Company to enhance hazard recognition and control with a focus on high energy materials. First, researchers are educated in hazard recognition and basic safety decision-making with decision aids made available to help them independently assess their work for hazards. These preliminary hazard assessments guide researchers in identifying and mitigating risks without direct expert engagement. When hazards are identified that researchers cannot resolve independently, reactive chemical subject matter experts and other safety personnel provide additional support. We share cases where hazard identification failed and the lessons, both technical and cultural, that resulted. Overall, the iterative learning process aims to optimize safety and use incidents as learning opportunities for future improvements.
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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 | 0.075 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.012 | 0.006 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.005 | 0.027 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.016 | 0.008 |
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".