Identifying further chemicals of emerging arctic concern based on ‘in silico’ screening of chemical inventories
Bibliographic record
Abstract
In the past 12 years several studies have screened lists of thousands of chemicals available in the industrial chemical inventories of the European Union, the USA and Canada with the goal of identifying and prioritizing chemicals which are persistent (P), bioaccumulative (B) and toxic (T). Most studies have selected chemicals based on whether their predicted P and B properties and their long-range transport potential exceed guideline thresholds for evaluation of persistent organic pollutants (POPs). A major goal of this study was to review this recent literature on computer-based or ‘in silico’ screening for POPs. A second goal was to review other approaches for finding previously unidentified chemicals of concern including targeted and non-target analytical approaches that might use lists of suspect chemicals developed from ‘in silico’ screening studies. Eight studies were reviewed along with several others which examined the screening process and its uncertainties. From these studies we assembled a list of 3421 chemicals, after removing duplicates and substances already on the Stockholm Convention on POPs. About 52% of these were halogenated, while 48% consisted of a broad range of non-halogenated organics. This list was then further analysed by calculating an overall “POPs score” for transport and accumulation in the Arctic for each substance using predicted partition coefficients, overall persistence, transfer efficiency, and bioaccumulation factor. A shorter list of twenty-five substances was developed based on their POPs score ranking. These substances had not been previously analysed in environmental media but were nevertheless on current or recent chemical inventories indicating significant commercial use.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".