Probabilistic observational method for estimating wall displacements in excavations
Why this work is in the frame
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Bibliographic record
Abstract
In this study, a novel method for updating the probability distribution of the maximum wall displacement at the ith excavation stage ([Formula: see text]) based on the measurements at earlier stages is proposed. The main novelty of the proposed method is in the updating procedure, which incorporates the correlation among the estimation errors at various stages. This “stage correlation” is evident from a database of wall displacement data from 22 case histories. By incorporating the stage correlation, it is shown that the uncertainty in [Formula: see text] can be effectively reduced through a Bayesian analysis. Furthermore, the calculation steps for such updating can be easily implemented by practical engineers because these calculation steps involve only algebraic computations and chart checking. Sophisticated analyses, such as solving an optimization problem (required by the maximum likelihood method) and probabilistic analyses are not necessary because all of the Bayesian analysis results are summarized in the charts.
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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 | 0.001 |
| 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 it