Assessment of Soil Vulnerability to Heavy-Metal Contamination Using Hierarchical Fuzzy Inference System
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The existence of contaminated areas and their impact on health and environment has led to the restraint of usage of these sites (brownfield) and a change in the politics of revitalization of these sectors of the urban area. The efficiency of these politics and investments for remediation of contaminated sites in the living environment relies on powerful and transparent environmental management and environmental impact assessment (EIA). In order to identify and prioritize the contaminated sites for remediation, EIA and exposure analyses, a fast user-friendly and applicable tool is essential. In this work, hierarchical fuzzy inference system (HFIS) was applied and a tool was developed to introduce the critical environmental and geo-environmental factors for decision-making purposes. This technique uses the most pertinent factors and parameters involved in heavy-metal contamination of topsoil to develop a powerful tool. This tool permits making environmental decisions regarding identification of the topsoil vulnerability to heavy metals.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it