Assessing Risks in Academic Labs: Uncertainties and Opportunities
Bibliographic record
Abstract
An institution as large as the University of Calgary (U of C) consists of more than 1000 laboratories with varying risks. The Environment, Health, and Safety officers cannot audit every laboratory regularly due to limited personnel. Moreover, the academic setting presents unique challenges not encountered in the industrial setting. Research activities are constantly progressing, new hazards are introduced, and members with different experiences rotate through the laboratories. This project proposes a methodology to assess and rank the various chemical hazards in the laboratories. A tool for analyzing the chemical risk levels present at each laboratory was developed, enabling the University to identify laboratories with higher levels of chemical hazards and prioritize them for audit. The CSLs and Hazard Codes matrix was created using an in-depth literature review, the Globally Harmonized System of Classification and Labelling of Chemicals, and the support of the safety specialists working at the University.
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 | 0.070 | 0.123 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.020 | 0.014 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.023 | 0.015 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".