Spatial Distribution of Hexachlorocyclohexanes in Agricultural Soils in Zhejiang Province, China, and Correlations with Elevation and Temperature
Bibliographic record
Abstract
Large quantities of hexachlorocyclohexanes (HCHs) were historically applied to soils in Zhejiang, a hilly province of eastern China, yet very limited information is publicly available for the present levels and residue characteristics of HCHs in the region. In this work, concentrations of HCHs and enantiomeric fractions (EFs) of α-isomer were analyzed in 58 agricultural soil samples (0-20 cm) collected in Zhejiang province. On the basis of the ratio of α-HCH/β-HCH and the fact that HCHs were banned in 1983 for agricultural use in China, fresh application of technical HCHs in a large quantity was unlikely in this province. Significant correlation was found between soil concentrations and elevation (R = 0.52, P < 0.001) and temperature (R = -0.55, P < 0.0001), but not between soil concentrations and total technical usage (R = -0.24, P = 0.1), suggesting a typical secondary distribution pattern. The soil residue inventories of HCHs derived from the relationship between concentration and elevation indicated 14.2 tons of HCHs left in agricultural soils in plain areas with the average elevation less than 100 m, and 61.6 tons of HCHs left in mountain soils with the average elevation higher than 100 m. It was also found that EFs of α-HCH were significantly negatively correlated with carbon biomass (C(bio)) in soils. This implies that C(bio) might have important impact on orientation and extent of enantioselective degradation of α-HCH in the region, which is, according to our knowledge, the first report of this kind.
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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 | 0.000 | 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".