[Identifying the Origins and Spatial Distributions of Heavy Metals in the Soils of the Jiangsu Coast].
Bibliographic record
Abstract
A total of 239 samples of surface soils were collected along the Xiangshui to Rudong coast, in Jiangsu Province, and analyzed for Cd, Cr, Cu, Hg, Ni, Pb, and Zn. A multivariate analysis was applied to identify the sources of heavy metals, and ordinary kriging was used to map the spatial distributions of the heavy metal concentration. The mean contents of Cd, Cu, Hg, Pb, and Zn in the surface soils of the Jiangsu Coastal Zone were higher than the background values of the Jiangsu Coastal Plain, which indicated that there were obvious accumulations of these heavy metals in surface soils; while the mean contents of Cr and Ni were lower than the background values. The contents of Cd, Cr, Cu, Pb, Ni, and Zn in soils that originated from marine deposition were significantly lower than those from alluvium and lagoon facies deposition, including the Yangtze River Delta deposition. Urban areas exhibited higher Cd, Cu, Hg, Pb, and Zn contents than other land covers. Cr and Ni were controlled by the parent material and seemed to originate from a natural source. Cd, Cu, Pb, and Zn were associated with the combination of parent material and anthropogenic inputs. Hg was dominated by atmospheric deposition related to various human activities. The high values of Cd, Cu, Pb, and Zn were distributed in the northern, western, and southern parts of the study area, and Hg exhibited high values around the urban areas in the western and southern parts.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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".