Assessment of Pollution and Environmental Status of Metals in Sediments of Subsidence-Land-Water-Ponds in Huainan Mining Area
Bibliographic record
Abstract
A case study on metals in sediment contaminated by coal mining activity was conducted through the survey of four subsidence-land-water-ponds in different coal mine regions with a variety of mining history in the Huainan mining area.Several metals(Cd,Cr,Cu,Pb,Zn,As,and Hg) in sediments were analyzed by IRIS Intrepid inductively coupled plasma-atomic emission spectrometry(ICP-AES) and hydride generation atomic fluorescence spectrometer(HG-AFS) instruments.A certified reference material(LKSD-1,Lake Sediment) from the National Research Council of Canada was used for analytical quality control,and the result was validated with respect to accuracy and precision.Sediment quality guidelines,geo-accumulation index method,and potential ecological risk assessment were adopted to assess pollution and potential ecological risks of the metals in sediments.Contribution and accumulation of metals derived from mining activity were revealed.The difference of pollution and potential ecological risk for individual metals were clearly illustrated through the gradually reducing trend of their concentration in sediments from mine regions with different mining history from 25100 years.However,the trends were different for the various individual elements.Pollution of all elements investigated ranged below middle level and potential ecological risk was at slight degree with the order of individuals metals CdHgCuAsPbCrZn.This study implied that long-term release of metals in association with 100-year typical low-sulfide coal mining history does not lead to serious sediment pollution in Huainan mining area.
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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 | 0.000 |
| 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".