Perspectives on Communicating Risks of Chemicals
Bibliographic record
Abstract
The Agrochemicals Division symposium "Perfecting Communication of Chemical Risk", held at the 244th National Meeting and Exposition of the American Chemical Society in Philadelphia, PA, August 19-23, 2012, is summarized. The symposium, organized by James Seiber, Kevin Armbrust, John Johnston, Ivan Kennedy, Thomas Potter, and Keith Solomon, included discussion of better techniques for communicating risks, lessons from past experiences, and case studies, together with proposals to improve these techniques and their communication to the public as effective information. The case studies included risks of agricultural biotechnology, an organoarsenical (Roxarsone) in animal feed, petroleum spill-derived contamination of seafood, role of biomonitoring and other exposure assessment techniques, soil fumigants, implications of listing endosulfan as a persistant organic pollutant (POP), and diuron herbicide in runoff, including use of catchment basins to limit runoff to coastal ecozones and the Great Barrier Reef. The symposium attracted chemical risk managers including ecotoxicologists, environmental chemists, agrochemists, ecosystem managers, and regulators needing better techniques that could feed into better communication of chemical risks. Policy issues related to regulation of chemical safety as well as the role of international conventions were also presented. The symposium was broadcast via webinar to an audience outside the ACS Meeting venue.
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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 | 0.035 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.018 |
| Scholarly communication | 0.020 | 0.028 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.023 | 0.022 |
| Insufficient payload (model declined to judge) | 0.020 | 0.003 |
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".