Fuzzy-Based Method to Evaluate Soil Corrosivity for Prediction of Water Main Deterioration
Why this work is in the frame
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Bibliographic record
Abstract
A fuzzy-based method is proposed to evaluate soil corrosivity from soil properties such as soil resistivity, pH, redox potential, sulfide content, and soil type. The fuzzy-based method considers three levels of soil corrosivity, noncorrosive, moderately corrosive, and corrosive. This is in contrast to the commonly used 10-point scoring (10-P) method that has only two classes, corrosive and noncorrosive. Membership functions for each of the soil properties are used to quantify their affinity to the level of soil corrosivity. These membership values form an evaluation matrix from which a weighted vector is developed using pair-wise soil property comparisons. The final classification is determined from the cross product of the weighted vector and the evaluation matrix. Two case studies are examined to validate the application of the proposed fuzzy-based method to predict soil corrosivity, and the results are compared to the 10-P method. Both case studies showed that the fuzzy-based method outperformed the 10-P method.
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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 | 0.013 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 it