37 Functional Requirements to Develop a new Risk Assessment Model for Exposure to Biological Agents
Bibliographic record
Abstract
Abstract Within Occupational Risk Prevention, the assessment of risks due to exposure to biological agents has received little attention compared to risks such as those arising from chemicals. This was the case until the impact of the COVID-19 pandemic highlighted the importance of health effects related to exposure to biological agents in the work environment. However, the tools for hazard inventory, risk assessment, and implementation of control measures for biological agents are scarce. There are many reasons that make it challenging for OHS professionals to assess the exposure to biological agents such as, little knowledge on the biological agents potentially present in the different economic activities, their effects on health, the lack of standardized methods for quantitative sampling or the lack of reference values available for most biological agents. Different qualitative risk assessment tools for biological agents have been developed over the last decades. Some examples are the Simplified Evaluation of the INSST (Spain), the Biogaval-Neo of the INVASSAT (Spain), the Bioaerosol Tool of the IRSST (Canada) or the RIE Method of the NKAL (The Netherlands). These tools differ from each other in terms of their scope and the parameters used to determine risk and were compared in order to better understand their limitations and applicability. After the comparison was carried out some additional specific parameters have been proposed as essential for the development of this new model. The proposed improvements could be implemented in the development of a new qualitative biological risk assessment model in the Stoffenmanager® tool.
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 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 | 0.001 | 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 | 0.000 | 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".