The current state of safety within the wet laboratories of a Canadian academic institution
Bibliographic record
Abstract
Working in an academic laboratory (lab) often involves handling hazardous substances (Shariff & Norazahar, 2012). These substances are dangerous due to their toxic, flammable, explosive, carcinogenic, pathogenic or radioactive properties (Furr, 2000). Therefore, it is crucial that those working in these environments do so safely. Recently, many researchers and students from various universities in the U.S. and globally have suffered severe injuries and fatalities from lab accidents. For example, in 2008 a lab fire at the University of California Los Angeles led to the death of a student(Van Noorden, 2011). Following this and other similar accidents that transpired afterwards, an international study was conducted to understand the state of safety within the wet labs of today’s universities(Van Noorden, 2013a). The findings revealed numerous safety gaps and an overall lack of a strong and positive safety culture within the labs (Benderly, 2013;and Schröder, Huang, Ellis, Gibson, & Wayne, 2016). Since the majority of the accidents and study reports were predominantly from the U.S., it is unknown if the same safety gaps and risks also exist in the wet labs of Canadian universities. Therefore, this research study examined the state of safety within the wet laboratories of a medium-sized Canadian university. This was achieved by: 1) conducting an inventory of the labs’ hazardous substances to identify their labeling and storage conditions, 2) inspecting the labs to identify potential hazards or risky conditions, and 3) surveying lab personnel to understand how safety is perceived and practiced. The results show several safety deficiencies and a negative perception on certain safety elements among the lab personnel. As in universities in the U.S. there is an overall need to enhance thecurrent culture of safety at this Canadian university.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".