Estimation of ultimate bond strength for soil nails in clayey soils using maximum likelihood method
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Bibliographic record
Abstract
This paper presents a maximum likelihood estimation of the ultimate bond strength for soil nails in clays. Both uncensored and censored ultimate bond strength data for soil nails are collected from the literature. Based on the concept of maximum likelihood, a log-likelihood function is constructed for estimating the mean and coefficient of variation (COV) of the ultimate bond strength jointly using the two types of data. The mean and COV are determined as the pair that maximises the log-likelihood function. Two distribution models (normal and lognormal) are used for the estimation. A comparison of the relative competence between the two candidate distribution models that are adopted for describing the collected uncensored and censored data is performed using the Bayesian Information Criterion. Example designs of soil nail walls against internal pullout limit state of nails and overall stability limit state are provided to demonstrate the benefit of taking censored data into account for estimation of the ultimate bond strength of soil nails.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 it