Chiral persistent organic pollutants as tracers of atmospheric sources and fate: review and prospects for investigating climate change influences
Bibliographic record
Abstract
Elimination of persistent organic pollutants (POPs) under national and international controls reduces “primary” emissions, but “secondary” emissions continue from residues deposited in soil, water, ice and vegetation during former years of high usage. Secondary sources are expected to dominate in the future, when POPs transport and accumulation will be controlled by air–surface exchange and the biogeochemical cycle of organic carbon. Climate change is likely to affect mobilization of POPs through, e.g., increased temperature, loss of ice cover in polar regions, melting glaciers and changes in soil and water microbiology which affect degradation and transformation. Chiral compounds offer advantages for following transport and fate pathways because of their ability to distinguish racemic (newly released or protected from microbial attack) and nonracemic (microbially altered) sources. Here we explain the rationale for this approach and suggest applications where chiral POPs could aid investigation of climate–mediated exchange and degradation processes. Examples include distinguishing agricultural vs. non–agricultural and recently used vs. residual pesticides, degradation and sequestration processes in soil, historical vs. recent atmospheric deposition, sources in arctic air and influence of ice cover on volatilization.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".