Discovering Chemicals of Emerging Arctic Concern: Application of New Analytical Approaches to Human Biomonitoring
Bibliographic record
Abstract
Different analytical strategies are used to discover chemicals of emerging concern in the Arctic. While traditional targeted analyses allow for the identification and quantification of chemicals with a priori knowledge of their presence, semi-targeted and untargeted analyses also permit samples obtained in the framework of human biomonitoring studies to be screened for the presence of unknown or unsuspected pollutants. We recently applied these different analytical strategies to human biomonitoring studies conducted in various regions of the Arctic. Chemical families targeted in plasma samples include polychlorinated biphenyls and chlorinated pesticides, polychlorinated dibenzo-p-dioxins and dibenzofurans as well as perfluorinated compounds. Targeted interrogation of the non polar purified extracts revealed the presence of chlorobenzenes, polycyclic aromatic hydrocarbons, polychlorinated naphthalenes, polychlorinated terphenylenes, short-chain chlorinated paraffins and natural halogenated compounds. Through untargeted analyses of extracts, thousands of entities are detected. Chemometric methods such as Kendrick mass defect plot and isotopic pattern detection can be used to attribute unknowns to the proper chemical family and facilitate compound identification. These innovative strategies will help identifying chemicals of emerging Arctic concern that should be included in future biomonitoring studies and considered for inclusion under the Stockholm Convention.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".