Enhancing Management of Time-Sensitive Chemicals in Higher Education: A Proactive Approach to Safety and Risk Reduction
Bibliographic record
Abstract
Time-sensitive chemicals are substances that can develop an explosion hazard when stored for prolonged periods. If not properly managed, they may deteriorate to create unknown hazardous conditions, increasing the risk of fires and explosions. When these chemicals are found under such unsafe conditions, they can no longer be handled safely by lab personnel and are outside the scope of general hazardous material disposal. Their removal requires costly, specialized contractors or high-profile interventions involving fire departments and police services. After a near-miss incident, the UCalgary EHS Lab Safety team prioritized revising the Lab Safety Program to focus on proactively managing time-sensitive chemicals. Key elements of the approach include increased awareness and education, improved guidance and resources, response processes to unsafe items, targeted risk-reduction initiatives, program sustainment, and continuous improvement. By sharing UCalgary’s approach, along with key challenges and lessons learned from campus-wide initiatives, near-miss incidents, and both planned and unplanned specialized contractor disposal events, we hope to support higher education institutions in strengthening their time-sensitive chemical management program.
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 | 0.013 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.004 | 0.021 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".