Estimation of Soil Moisture and Earth Resistivity Using Wenner’s Method and Machine Learning
Bibliographic record
Abstract
The research presented in this dissertation discusses a novel approach to address the corrosion of underground metallic structures. The system consists of using Wenner’s four electrodes method to measure the electrical resistivity of the soil (e.g., clayey silt and clay), applying two machine-learning algorithms (k Nearest Neighbor (Knn) and Supervised Vector Machine (SVM)) to predict the type of soil, and help engineers to leverage the extracted parameters to select the best material that withstands corrosion in that specific environment. A dataset of 162 sample points was obtained from different kinds of literature (142 training, and 20 testing points). The results show that given the electrical resistivity of soil and its moisture, the k nearest neighbor model is capable of predicting the type of soil with accuracy, error rate, sensitivity, specificity, and precision of 70%, 30%, 64%, 83%, and 90% respectively. In contrast, the support vector machine model was not able to perform soil prediction, presenting an error rate and accuracy of 44.1% and 55.9 % respectively. This dissertation also provides suggestions such as increasing the number of sample points to improve the performance of the machine learning algorithms.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".