Screening Chemicals for the Potential to be Persistent Organic Pollutants: A Case Study of Arctic Contaminants
Bibliographic record
Abstract
A large and ever-increasing number of chemicals are used in commerce, and researchers and regulators have struggled to ascertain that these chemicals do not threaten human health or cause environmental or ecological damage. The presence of persistent organic pollutants (POPs) in remote environments such as the Arctic is of special concern and has international regulatory implications. Responding to the need for a way to identify chemicals of high concern, a methodology has been developed which compares experimentally measured properties, or values predicted from chemical structure alone, to a set of screening criteria. These criteria include partitioning properties that allow for accumulation in the physical Arctic environment and in the Arctic human food chain, and resistance to atmospheric oxidation. Atthe same time we quantify the extent of structural resemblance to a group of known Arctic contaminants. Comparison of the substances that are identified by a mechanistic description of the processes that lead to Arctic contamination with those substances that are structurally similar to known Arctic contaminants reveals the strengths and limitations of either approach. Within a data set of more than 100,000 distinct industrial chemicals, the methodology identifies 120 high production volume chemicals which are structurally similarto known Arctic contaminants and/or have partitioning properties that suggest they are potential Arctic contaminants.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".