Assessment of Potential Heavy Metal Contamination in the Agricultural Soils Based on Various Improved Evaluation Methods in Beijing, China
Bibliographic record
Abstract
Abstract The evaluation of the soil contaminated by heavy metals can help to judge whether the soil meets the standard and whether the pollution will threaten human health and the ecological environment. In this study, the farmland soil from eight districts in Beijing was used as the research object, and the concentration of heavy metal elements, Pb, As and Cd in the soils and agricultural products were analyzed. The analysis results showed that: (1) The evaluation based on the improved Hakanson method suggested that the crops exhibit a significantly higher ability to absorb Cd than to absorb Pb and As. Pb, As and Cd are all at normal level of ecological risk; among them, Cd is mainly in a moderate ecological risk, without strong ecological risk. (2) Based on the Improved analytic hierarchy process(AHP) of evaluation, 0.2317 is the average value of the integrated index of heavy metal pollution of soil in the study area, which is a mild level of pollution. (3) Through the calculation of various parameters in the Influence index of comprehensive quality(IICQ) of soil and agricultural products, it was found that 0＜IICQS＜1, suggesting that the environmental quality of soil is at a clean level. In summary, the pollution of heavy metals Pb, As and Cd in the farmland soils and crops in the eight districts of Beijing, including Fangshan, Daxing, Shunyi, and Shijingshan is at a low level, and no significant impact has been brought to the surrounding environment.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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".