Diagnosis of embankment dam distresses using Bayesian networks. Part II. Diagnosis of a specific distressed dam
Why this work is in the frame
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Bibliographic record
Abstract
Based on prior information on common characteristics of dam distresses extracted from the dam distress database described in a companion paper, this paper attempts to extend the technique of Bayesian networks to the diagnosis of a specific distressed dam. The diagnosis is conducted by combining two sources of information, i.e., global-level knowledge from the database and project-specific evidence. Based on results of the diagnosis, key distress factors for a specific dam can be identified and suitable remedial measures can be suggested. Further, the Bayesian network analysis is conducted to evaluate the effectiveness of the adopted remedial measures. A case study on the diagnosis of a distressed embankment dam, Chenbihe Dam, with seepage problems is presented to illustrate the methodology. In this case study, the observed leakage rates, seepage exit locations, and boundary conditions of the embankment are used as project-specific evidence.
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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) | 0.001 | 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