Are There Other Persistent Organic Pollutants? A Challenge for Environmental Chemists.
Bibliographic record
Abstract
The past 5 years have seen some major successes in\nterms of global measurement and regulation of persistent,\nbioaccumulative, and toxic (PB&T) chemicals and\npersistent organic pollutants (POPs). The Stockholm\nConvention, a global agreement on POPs, came into force\nin 2004. There has been a major expansion of measurements\nand risk assessments of new chemical contaminants in the\nglobal environment, particularly brominated diphenyl\nethers and perfluorinated alkyl acids. However, the list of\nchemicals measured represents only a small fraction of\nthe approximately 30,000 chemicals widely used in commerce\n(>1 t/y). The vast majority of existing and new chemical\nsubstances in commerce are not monitored in environmental\nmedia. Assessment and screening of thousands of\nexisting chemicals in commerce in the United States,\nEurope, and Canada have yielded lists of potentially persistent\nand bioaccumulative chemicals. Here we review recent\nscreening and categorization studies of chemicals in\ncommerce and address the question of whether there is\nnow sufficient information to permit a broader array of\nchemicals to be determined in environmental matrices. For\nexample, Environment Canada's recent categorization of\nthe Domestic (existing) Substances list, using a wide array\nof quantitative structure activity relationships for PB&T\ncharacteristics, has identified about 5.5% of 11,317 substances\nas meeting P & B criteria. Using data from the Environment\nCanada categorization, we have listed, for discussion\npurposes, 30 chemicals with high predicted bioconcentration\nand low rate of biodegradation and 28 with long range\natmospheric transport potential based on predicted\natmospheric oxidation half-lives >2 days and log air−water partition coefficients ≥5 and ≤1. These chemicals\nare a diverse group including halogenated organics, cyclic\nsiloxanes, and substituted aromatics. Some of these\nchemicals and their transformation products may be\ncandidates for future environmental monitoring. However,\nto improve these predictions data on emissions from\nend use are needed to refine environmental fate predictions,\nand analytical methods may need to be developed.
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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 | 0.000 | 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 | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".