Uncertainty Representation in Health Risk Assessment of Contaminated Sites
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Human health risk assessment is a complex process consisting of four main steps. (1) hazard identification; (2) exposure analysis; (3) toxicity assessment; and (4) risk characterization. At each of these steps, there are many types of uncertainty, which includes non random variables, subjectivity and vagueness in information as well as random variables such as body weight, inhalation rate, and the exposure periods. Uncertainties in risk assessment are categorized as qualitative or quantitative with conservative estimates being used for risk assessment. US EPA guidelines recommend the use of 95th percentile values for the calculation of reference dose. It has been shown that this may lead to an overestimation of the risk value (Burmaster and Anderson 1993). Though the methods can be justified to be protective of sensitive sub-populations, uncertainty is not completely represented. The objective of this paper is to investigate the different uncertainties that play a role in contaminated site risk assessment and the application of various theories that could be employed to reduce the overall uncertainty.
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.
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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 it