Evaluation of Lead in Arable Soils, China
Bibliographic record
Abstract
Lead (Pb) contamination in arable soils is one of the most serious ecological problems due to its high toxicity on human health. Thus, we need to understand the concentration level, contaminated area, and spatial distribution of Pb in arable soils on regional or national scale. This paper reviewed the studies on Pb concentrations throughout Chinese arable soils, based on relevant 537 studies from 2002 to 2014. The results showed that the average Pb concentration was 34.41 mg/kg, higher than its background of 23.50 mg/kg, indicating that Pb has been introduced into soil from exterior sources. Mining and smelting activities, irrigation by wastewater, and urban development greatly contributed to Pb accumulation in arable soils. North China had lower Pb concentrations than the south, and many hotspots existed on the Pb concentration map due to mining and smelting activities. On the provincial scale, arable soils in Yunnan, Guangxi, and Shaanxi Provinces were moderately polluted by Pb, Gansu and Shaanxi Provinces were slightly affected by Pb, while the other provinces showed relative safe levels.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".