Using complex permittivity and artificial neural networks to identify and classify copper, zinc, and lead contamination in soil
Bibliographic record
Abstract
Complex permittivity measurements combined with artificial neural networks (ANNs) are investigated as a method for assessing and identifying heavy metal contamination in soil. The measurements are carried out with a custom-built device on 164 compacted samples of a natural clayey soil, artificially contaminated with various simple salts including heavy metals (Cu, Zn, and Pb). The soil samples are prepared by mixing solutions of the various salts with the soil at various concentrations and water contents. A database has been set up consisting of complex per mittivity measurements made between the frequencies of 200 and 500 MHz and measured physical and chemical properties of the soil samples. Using this database as input, two ANN models are designed, the first to detect the presence or absence of heavy metals in the soil samples and the second to determine whether the heavy metal, if present in a given sample, is Cu, Zn, or Pb. Both ANN models perform reasonably well. Overall, the first model is able to detect the presence of heavy metals in 92.7% of cases, and the second is successful in distinguishing the particular type of heavy metal in 76.4% of all the samples containing heavy metals. These encouraging results underscore the potential of complex permittivity and ANNs as promising tools for nondestructive subsurface contamination assessment.Key words: heavy metals, subsurface contamination, complex permittivity, artificial neural networks, contaminant detection.
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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 | 0.000 | 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) | 0.000 | 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".