[Sources, Distribution of Main Controlling Factors, and Potential Ecological Risk Assessment for Heavy Metals in the Surface Sediment of Hainan Island North Bay, South China].
Bibliographic record
Abstract
, respectively. High contents of Cr, Cu, Hg, Pb, and Zn are mainly distributed in estuaries and harbors. The content of As increased gradually from south to north in the research area, while Cd gathered mainly in Haikou Bay. The source of Cu, Zn, Hg, Pb, Cr, and Cd in sediment was terrestrial input, which was controlled by anthropogenic pollution and migration of weathered products from mother rock. The source of As could be overseas material input. The grain size of sediment was the main factor controlling the contents of Cu, Zn, Hg, Pb, and Cr, and it was found that sediments with a finer grain size have more heavy metals adsorbed. The high As content was controlled mainly by the natural geological background factor, while the content of Cd reflected the difference in regional pollution caused by differing development in cities. Three surface sediment samples were evaluated as class Ⅱ of the National Marine Sediment Standard due to the content of As, while six surface sediment samples were evaluated as class Ⅱ due to the content of Cr, of which the pollution degree was moderate. The concentrations of Cd, Cu, Hg, Pb, and Zn in all of the 159 sediment samples fall in the Ⅰ class, of which the pollution degree is low. The degree of pollution for seven heavy metals were arranged in the following order:As > Cr > Pb > Zn > Cu > Cd > Hg. The potential ecological risk assessment results indicated that all heavy metals in surface sediment caused low levels of pollution generally, and that each heavy metal element was at a low ecological risk level. The order for the ecological risk of the seven heavy metals was:As > Hg > Cd > Pb > Cu > Cr > Zn; therefore, As was the main ecological risk factor. In general, the potential ecological risk for heavy metals was low, which illustrated that the marine environment in the study area was excellent.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".