Persistent organic pollutants and mercury in the Himalaya
Bibliographic record
Abstract
Persistent organic pollutants and mercury are important contaminants due to their persistence in the environment and potential toxic effects on ecosystems and humans. Concerns related to these contaminants are particularly pertinent in Asia where the use of pesticides and mercury emissions have been increasing dramatically due to changing agricultural practices and rapidly expanding industrialization. Based on studies in European and North American mountain regions, evidence is increasing that alpine regions function as regional convergence zones for selected organic pollutants due to an effect called orographic cold trapping. It is hypothesized that such an effect may be particularly pronounced in the Himalaya because of dramatic elevational temperature and precipitation gradients relative to contaminant source regions in its immediate vicinity, and because of the regional monsoon system that has been shown to deliver particles and inorganic air pollutants to higher altitudes. A review of studies of persistent organic pollutants and mercury in the Himalayan ecosystem reveals that measurements of these contaminants are sparse and rarely adhere to strict quality control procedures, making it difficult to judge whether relatively high concentrations reported for these materials are indeed an indication of efficient transfer of toxic contaminants to Himalayan ecosystems. Knowledge gaps are identified and suggestions are made for research that would allow for the testing of the hypothesis that the Himalaya is an important cold trap for semi-volatile organic contaminants and mercury.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".