<scp>Results of a</scp> 2020 <scp>Survey on Reporting Requirements and Practices for Biocontainment Laboratory Accidents</scp>
Bibliographic record
Abstract
Biosafety laboratory accidents are a normal part of laboratory science, but the frequency of such accidents is unclear due to current reporting standards and processes. To better understand accident reporting, a survey was created, with input from ABSA International, which included a series of questions about standards, requirements, and likely motivations for reporting or nonreporting. A total of 60 biosafety officers completed the survey. Respondents reported working with more than 5,000 people in laboratories, including more than 40 biosafety level 3 or animal biosafety level 3 laboratories, which work with higher-risk pathogens. Most of the respondents were located in the United States, Canada, or New Zealand, or did not identify their location. Notable results included that 97% of surveyed biosafety officers oversee laboratories that require reporting exposure to at least some pathogens. However, 63% relayed that the reports are not usually sent outside of the institution where they occurred. A slight majority (55%) stated that paper reports were used, with the rest reporting they used a variety of computer systems. Even in laboratories that used paper-based reporting systems, 67% relayed that these reports were used alongside, or entered into, a digital system. While 82% of these biosafety officers agreed that workers understood the importance of reporting for their own safety, 82% also agreed that a variety of disincentives prevent laboratory workers from reporting incidents, including concerns about job loss and loss of funding.
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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.006 |
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".