Modeling Global Environmental Fate and Quantifying Global Source–Receptor Relationships of Short-, Medium-, and Long-Chain Chlorinated Paraffins
Bibliographic record
Abstract
Decades-long emissions and long-range transport of chlorinated paraffins (CPs) have resulted in their pervasive presence in the global environment. The lack of an understanding of the global distribution of short-, medium-, and long-chain CPs (SCCPs, MCCPs, and LCCPs) hinders us from quantitatively tracing their origins in remote regions. Using the BETR-Global model and historical emission estimates, we simulate the global dispersion of CPs from 1930 to 2020. Whereas contamination trends in the main contaminated regions (East Asia, Europe, North America, and South Asia) diverge, CP concentrations in the Arctic, Antarctica, and the Tibetan Plateau all increase. By 2020, East Asian, European, and North American emissions contributed 38%, 26%, and 18% of CP contamination in the High Arctic, respectively, while Southern hemispheric emissions and emissions around the Tibetan Plateau primarily contribute to CP contamination in central Antarctica and on the Plateau, respectively. Our results emphasize the important contribution of (i) European and North American emissions to historical CP contamination in remote regions and current MCCP and LCCP contamination in the High Arctic and (ii) East Asian emission to current SCCP and MCCP contamination of all three remote regions. These results can help to evaluate the effectiveness of potential global and regional CP emission-reduction strategies.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".