Heuristic Topological Graph Convolutional Network for Risk Prediction of Potentially Toxic Elements in Cultivated Soils
Bibliographic record
Abstract
Contamination of cultivated soils with potentially toxic elements (PTEs) poses a growing threat to global food security. Although existing risk assessments have examined the accumulation and toxicity of PTEs, their dynamic interplay with multidimensional drivers has remained inadequately characterized. Here, an innovative heuristic graph convolutional network (GCN) model is introduced by integrating adaptive graph topology with quantified directional feedback optimization to improve ecological risk prediction. Leveraging 466 spatially resolved soil samples and 28 environmental drivers of a typical rice production area Yangtze River Basin in China, the heuristic GCN model outperformed traditional approaches by 23.1% in predictive accuracy. A three-phase heuristic algorithm pruned 85.5% of spurious edges in the topological graph, and GCN adaptively quantified the directional feedback between environmental drivers and ecological risk. Topological networks and feature importance analysis jointly identified pH, base saturation, calcium carbonate, exchangeable bases, and soil organic carbon as pivotal regulators acting alongside geological factors. By linking mechanistic soil chemistry with machine-learning-based causal inference, our model supports streamlinedly simplified, directionally quantified, and dynamically adapted ecological risk prediction. This enables the screening of the most efficient pathway of risk management and provides more precise and integrated strategies for ecological risk control in agroecosystems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 teacher head, 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".