Potential Implications of Inappropriate Assumption Selection on Environmental Modelling Results: An RBCA Case Study
Bibliographic record
Abstract
In the past decade, the field of environmental risk assessment has seen vast improvements in the technologies and tools available to aid with quantification of human health and ecological risks. The development of publicly-available computational modelling tools, such as those based upon the risk based corrective action (RBCA) framework, have allowed those with a potentially less sophisticated understanding of the scientific rationale, equations and methodologies used to simulate environmental fate, toxicology and risk characterization processes to conduct and complete multimedia environmental risk assessments for regulatory approval. However, in many cases, this lack of understanding may result in the selection of inappropriate parameter data (i.e., chemical-, receptor-, or scenario-specific data), unknowingly increasing the uncertainty inherent within the overall assessment of potential risk. Accordingly, the use of inappropriate assumptions and parameters can result in an increase or decrease in the estimated risk, with significant implications on remediation costs or, more importantly, human or environmental health. Three case studies will be presented to illustrate the importance surrounding the selection of appropriate data input parameters, and the potential implications of this selection process on the results of an environmental risk assessment.
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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 | 0.019 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".