Laboratory accidents and biocontainment breaches: Policy options for improved safety and security
Bibliographic record
Abstract
Laboratory accidents can have serious and potentially catastrophic consequences. Laboratory-acquired infections and other biocontainment breaches, both of which can result in the escape of dangerous pathogens into the community, have the potential to trigger outbreaks with wide-ranging implications. Incidents like these are of concern to a broad range of stakeholders beyond the scientific research community – including policymakers, law enforcement agencies and the general public. In the last few decades, regulation has increased and biosafety guidance has been strengthened. However, such accidents continue to occur with regularity and most are caused by avoidable human error and inadequate procedures. This paper discusses the findings of a new review of all reports of laboratory accidents worldwide that were published between 2000 and 2021. The review highlights that few countries require the reporting of laboratory accidents, and that what is reported is a significant understatement of the scale of the problem. To improve safety and security where pathogens are involved, understanding of the full scale of laboratory accidents and their causes is essential, and a sustainable risk-based approach – that takes full account of local context, and can be tailored – must be at the heart of policymaking in the sector.
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 | 0.024 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.013 | 0.022 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.019 | 0.010 |
| Insufficient payload (model declined to judge) | 0.029 | 0.005 |
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".