Risk assessment: some new challenges associated with PCBs, Arsenic, and
Bibliographic record
Abstract
ABSTRACT: A number of relatively new challenges associated with risk assessment and management of PCBs, arsenic and hydrocarbons are highlighted. The limitations of evaluating the fate and transport of PCBs using a “total PCB ” approach, as opposed to a congener-specific approach, are examined. It is also indicated that there is a growing need to consider potential risk associated with the effects of processes such as biologi-cal activity rendering arsenic more mobile and/or toxic. The need to examine the true effectiveness and the long term risk associated with the use of soil-bentonite walls as a barrier to advective and diffusive transport of VOCs is discussed. Finally the challenge of dealing with contamination in extreme environmental condi-tions (e.g., northern Canada) and the need to adopt a robust risk assessment and management approach in dealing with these problems are highlighted. 1 INRODUCTION The concepts of risk and its management have had a long history in geotechnical engineering. In 1964, Casagrande devoted his 2nd Terzaghi Lecture to the theme of the role of the “calculated risk ” in earth-works and foundation engineering (Casagrande 1965). The main points made by Casagrande in that lecture were that risks are inherent in any project, that the existence of such risks should be recog-nized, and that steps- representing a balance be-tween economy and safety- should be systemati-cally taken to deal with these risks. From the early 1970s, probability theory was in-troduced to quantify risk. Over the years, a number of researchers have made contributions in this area
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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 | 0.049 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.010 | 0.013 |
| Insufficient payload (model declined to judge) | 0.004 | 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".