Cold-Trapping of Persistent Organic Pollutants in the Mountain Soils of Western Sichuan, China
Bibliographic record
Abstract
In the Chinese province of Sichuan steep mountains rise from the very densely populated and intensely cultivated Chengdu basin more than 4000 elevational meters to the Tibetan Plateau. This steep physical gradient is exceptionally well suited to investigate the transport of persistent pesticides and other organic contaminants from low to high elevations. In spring and autumn 2006, 25 soil samples were taken at five elevations ranging from 2636 to 4479 m along the East-facing slope of Balang Mountain in Wolong Nature Reserve. Analysis of soil extracts was done by gas chromatography-high resolution mass spectrometry. Whereas hexachlorobenzene (HCB), hexachlorocyclohexanes (HCH), and dichlorodibenzotrichloroethane and its degradation products (DDTs) were present at levels of a few ng/g, only two light PCB congeners were detected at levels below 1 ng/g in soil. Soil concentration for all analytes increased significantly and exponentially with altitude. The rate of concentration increase, expressed quantitatively through the slope of the linear regression between the logarithm of the concentrations and altitude, increases along the sequence HCB < PCB < HCH < or = DDT. This trend is consistent with, and therefore lends additional observational support to, a mountain cold-trapping mechanism based on the temperature dependence of precipitation scavenging.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".