Ecological Risk Assessment of Soil Heavy Metals and Pesticide Residues in Tea Plantations
Bibliographic record
Abstract
Tea plantations have used many synthetic chemicals to ensure performance and control of pests. This has led to increased contamination of soils and reduced tea growth. We assessed the levels of heavy metals, including Cd, Cr, Pb, Cu, Ni, Zn, Hg, As, and pesticide residues, such as HCHs, biphenyl chrysanthemum ester, methamidophos, imidacloprid, permethrin, in the soil of tea plantations of Taiwan, Tibet, Guangdong, and Fujian. The Potential Ecological Risk Index and the Nemerow comprehensive pollution index were used to analyze the data. The results showed that risk indices in Tibet, Guangdong and Fuzhou were considered as moderate ecological harm level. Ecological risk assessment index of Anxi organic and Anxi conventional tea gardens suggested a “low” risk level. The Nemerow comprehensive pollution indices for soil pesticide residues in the tea plantations of Taiwan, Tibet, Anxi organic and Anxi conventional were considered mild. Guangdong and Fuzhou had values suggesting “slight pollution” levels. According to National Soil Environmental Quality Standard (GB15618-1995), soil in tea plantations in Taiwan, Tibet, and Anxi conventional matched the national first grade of soil quality and those from Guangdong, Fuzhou, and Anxi organic tea garden matched the national second grade.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".