The Need for Mandatory Academic Laboratory Sustainability Training – A Fume Cupboard Case Study
Bibliographic record
Abstract
While scientific research is paramount to understanding the solar system, ecosystem, human disease and cures (etc) it continues to contribute to human-caused climate change. Scientists are becoming increasingly aware of the carbon footprint associated with their research and recognise the need to work more efficiently with their resource use and laboratory operations. University laboratories are spaces that allow for research to be carried out safely, however, they consume five times more energy per square meter than office buildings. Fume cupboards are amongst the most energy intensive equipment and thus are a dominant factor when working towards creating safer and greener laboratories. In this paper, we report on the gas, electricity, carbon and financial savings derived by upgrading 105 constant air volume fume cupboards to variable air volume systems. We also report on the frequency of fume cupboard use by research staff, postgraduate students, and their overall understanding of fume cupboard best practice operations. The results reflect that while savings were achieved, they were lower than predicted. A factor to this may be poor student and staff understanding of how fume cupboards work resulting in their incorrect usage, therefore hampering sustainable progression. This study highlights that a major gap exists between laboratory technical upgrades and researcher awareness of proper and safe equipment use and operation. To overcome this, we propose that in addition to health and safety training, mandatory laboratory sustainability operations training should be provided to all laboratory users.
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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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".