Prediction of standard penetration test <i>N</i>-value from dynamic probing light <i>N</i>-value using ANFIS and multiple regression models
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
Standard penetration test (SPT) is one of the most widely used tools to predict the soil properties. In recent years, the dynamic probing light (DPL) test has been performed more frequently for geotechnical applications because it is more cost-effective and fast. Since the majority of empirical equations of soil properties are related to SPT N-value, it is beneficial to find the best correlation between SPT and DPL N-values. In this study, the adaptive neuro-fuzzy inference system (ANFIS) and multiple regression (MR) are used to predict the correlation between SPT and DPL N-values. To achieve this goal, the soil properties of 64 sample specimens of silty clay were calculated at various depths. Three parameters including depth, total density, and DPL N-value were chosen as the input of the models. Results show that both methods estimate the correlation between SPT and DPL N-values precisely. However, ANFIS predicts more accurately than multiple regression.
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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 | 0.000 | 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 | 0.001 |
| 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 it