Risk assessment for environmental contamination: an overview of the fundamentals and application of risk assessment at contaminated sites
Bibliographic record
Abstract
Many jurisdictions throughout Canada and the United States allow the decision to proceed with an active site cleanup, an evaluation or monitoring program, or site closure to be determined from the results of a risk assessment. Risk assessment can protect both human health and the environment while providing significant savings to industry and government by reducing unnecessary environmental expenditures and allowing for the more efficient allocation of resources. Risk assessment is a dynamic process which is evolving as new methods for contaminant fate and transport modelling, more complete toxicological data, and more standardized statistical methods become available. Principal methodologies for conducting risk assessments are provided by the United States Environmental Protection Agency (USEPA), the American Society for Testing and Materials (ASTM), and the Canadian Council of Ministers of the Environment (CCME). Risk assessment methodologies include both "forward" risk calculations and "reverse" risk calculations. Uncertainties inherent in risk assessment methodologies can be difficult to quantify and are dealt with by incorporating conservative assumptions throughout the risk assessment process. Institutional controls may be required to ensure the continued validity of assumptions. There is a trend toward the increased application and acceptance of risk assessment, although Canada remains well behind the United States. To ensure the expanded use of risk assessment in Canada, more training for both regulators and environmental professionals, increased public awareness, and the adaptation of the risk assessment methodologies being applied in the United States to reflect the environmental policies and technical concerns of interest across Canada will be required.Key words: risk assessment, petroleum, risk-based corrective action, site remediation, remediation objectives, Canada, institutional controls, ethics.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".