Column Experiment Results on Metal Ion Migration at the Xiangtan Manganese Mine Wasteland in Central South China
Bibliographic record
Abstract
Abstract- Column experiment simulations were carried out to determine potential metal ion migration in order to establish the environmental impact of a manganese mine wasteland and to understand the transport dynamics of heavy metal migration by way of two different soil types (tailings and tailings mud) within the Xiangtan Manganese Mine wasteland. Simulations were conducted at the Research Section of Forest Ecology, CSUFT. Results show a lower water infiltration rate for tailings mud compared to tailings. Considerably higher water content was found in tailings mud, and leachate from tailings mud contained considerably higher metal ion concentrations. Mg, Mn, Ca, K, Zn, Ni, Pb, Fe, Cu, and Cd is the order of metallic ion concentrations from high to low found in the leachate of tailings. However, this order altered slightly during instances when a lower Mn concentration (compared to Ca) was found. Almost all tailings mud metal ion concentrations in the leachate were higher compared to tailings, especially in the case of Mn, which was higher by a factor of 25. K concentrations were also higher by a factor of 10. The primary metallic ions found in the leachate for both tailings mud and tailings were Mg, Mn, Ca, and K. Almost all metal ion concentrations for both leachates decreased sharply with an increase in the number of leaching events. Mg, Mn, Ca, and K concentrations were the highest during the first leaching event and decreased sharply during the following two leaching events. However, Ni, Pb, Fe, Cu, and Cd concentrations remained almost constant at very low concentrations, especially for the tailings leachate. These results, together with a detailed field investigation of prevailing conditions, would be useful for mine wasteland phytoremediation initiatives, and contribute to the development of ecological restoration.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".