Input of organochlorine compounds by snow to European high mountain lakes
Bibliographic record
Abstract
Summary 1. Persistent organic pollutants (POPs) can be trapped by and accumulate in cold regions. To understand POP accumulation in temperate high mountain lakes, we collected samples of snow from the catchments of several high mountain lakes in Europe. 2. Organochlorine compounds (OCs) are regularly found in snow collected in European high mountain sites. Polychlorobiphenyls (PCBs) were found in all samples examined. Hexachlorocyclohexanes (HCHs) are also common whereas DDTs and hexachlorobenzene were found less frequently. 3. Comparison of the concentrations of these pollutants in snow with OC levels in the waters of these lakes or atmospheric deposition during cold periods shows that the snowpack constitutes a significant seasonal PCB reservoir in all catchments and that snow trapping is a major mechanism for the incorporation of HCHs in the lakes situated in the coldest sites, e.g. Gossenköllesee in the Alps. 4. Correlation of the concentrations of the PCB congeners in snow versus mean annual winter temperature shows higher accumulation at lower temperatures. The less volatile PCBs exhibit higher temperature dependences than the more volatile congeners. This trend differs from altitudinal dependences observed in other sites such as Canada and may be related to the specific range of winter temperature in each area of study.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".