In situ passivation effect of fertilizer passivation solutions with various pH on Pb-F contaminated soil
Bibliographic record
Abstract
The Maoniuping rare earth mine in Mianning, Liangshan Prefecture, is the largest rare earth deposit in China and the second largest rare earth deposit in the world. During the mining of rare earths, F and Pb, both types of heavy metal waste can directly or indirectly enter the soil, causing local soil contamination, which in turn poses a threat to the health of local residents. In this study, soil samples were incubated with fertilizer passivation solutions with pH values of 4, 5, 6, 7, 8, and 9, with the soil water content adjusted to 50% of the maximum water holding capacity in the field. The Pb-F contamination of the soil samples was analyzed to determine the existing states of the soil F and Pb and to study the remediation effect of fertilizer passivation solutions on the Pb-F contaminated soil. The results showed that under different passivation conditions, the pH, Pb, and F of the soil substantially changed and the passivation effect was enhanced over time. The pH of the soil significantly increased. The analysis of the effects of F and Pb treatment revealed that when the passivation fertilizer solution of pH 5 was used, the available Pb and F in the soil decreased the most, and the residual state of Pb and F increased to 77.86% and 57.24%, respectively.
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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 | 0.000 | 0.000 |
| 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) | 0.001 | 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".