Variable Significance Determination Utilizing Extended CHAID Method in Fiber Improvement of Fine Soils
Bibliographic record
Abstract
Adding fiber to reinforce soil is a technique widely used at geotechnical works due to the possibility of a considerable increase in soil’s mechanical properties. One way to evaluate such improvement is to compare the features before and after the addition, utilizing the unconfined compressive strength (UCS) test. However, the results of this test can be biased by soil variability, fibers characteristics, and test errors. The objective of the present work is to analyze which variable most influences the results of UCS tests and evaluate the relationship between the soil strain and its strength alterations due to fiber. In this way, the extended chi-square automatic interaction detector (CHAID) method was adopted on UCS samples obtained from a database established from papers, theses, and dissertations. This process concluded that the type of fiber used is the variable with the highest significance among the studied ones. Furthermore, the regression of the polypropylene (PP) demonstrated a linear tendency and a strong correlation coefficient, validating the hypothesis that the sample’s strength has an immediate relation with its strain.
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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 | 0.001 | 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".