Prediction of friction capacity of driven piles in clay using the support vector machine
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 support vector machine (SVM) is an emerging machine learning technique where prediction error and model complexity are simultaneously minimized. This paper examines the potential of SVM to predict the friction capacity of driven piles in clay. This SVM is firmly based on the statistical learning theory and uses the regression technique by introducing accuracy (ε) insensitive loss function. The results are compared with those from a widely used artificial neural network (ANN) model. Overall, the SVM showed good performance and is proven to be better than ANN model. A sensitivity analysis has been also performed to investigate the importance of the input parameters. The study shows that SVM has the potential to be a useful and practical tool for prediction of friction capacity of driven piles in clay.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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