Silicon and heavy metal dynamics in soil-rice systems: A taihu lake plain case study on silicon depletion
Bibliographic record
Abstract
This study addresses the critical issue of heavy metal contamination (Ni, Cu, As, Cd, Pb) in rice cultivation, with particular focus on silicon-deficient paddy soils in the Taihu Lake Plain, where Si NaAc is 87.97 mg kg −1 . While silicon (Si) supplementation has demonstrated efficacy in mitigating heavy metal stress in plants, its effectiveness in Si-deficient soil-rice systems remains insufficiently characterized, particularly at the field scale. Our investigation of four typical crop rotation systems revealed significant contamination risks from As and Cd, with plant-available silicon (PaSi) predominantly influencing the active fractions of these metals. Key factors governing heavy metal accumulation in brown rice included soil metal content (HMs Soil ), soil PaSi (PaSi Soil ), and plant Si (Si Plant ), exhibiting strong predictive power for As (R²=0.69), Cd (R²=0.51), and Cu (R²=0.59) accumulation. Structural equation modeling identified dual mechanisms of Si-mediated metal regulation: direct modulation in the Husk-Brown Rice transfer pathway and indirect suppression through Root-Straw translocation. These findings elucidate Si's pivotal role in impeding heavy metal transfer from soil to edible grains, highlighting the potential of strategic Si fertilization as an effective agronomic intervention for minimizing dietary heavy metal exposure and enhancing food safety in contaminated rice production systems.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".