Reliability of Technical Measures for Erosion and Sediment Control in Construction Worksites
Bibliographic record
Abstract
Abstract This study investigates reliability engineering approaches to structure the assessments of the reliability of erosion and sediment controls for two culvert replacement projects. Construction projects require that a System of Erosion and Sediment Controls (SESC) meet water quality guidelines through monitoring activities that seldom addresses the reliability of the system. The reliability analysis uses the maximum increase of 8 Nephelometric Turbidity Units (NTU) from background levels (∆Tu) for a short-term exposure (< 24 h) from the Canadian Water Quality Guidelines for Total Particulate Matter. The analyses are conducted for the early, stable, and wear-out reliability periods with the addition of catastrophic periods. A reliable SESC is expected to perform its intended function during the stable periods. Aligned with reliability engineering theory, the rates of exceedance for the stable periods of each project are the lowest when compared to the other reliability periods (i.e., 0.026 and 0.027 ∆Tu > 8 NTU event per hour). In addition, the average amount of time ∆Tu ≤ 8 NTU for the stable periods is the longest compared to the other reliability periods (i.e., 33.6 h and 34.0 h). As well, the average amount of time when ∆Tu > 8 NTU are 2.1 h and 1.6 h for the stable periods. The SESC did not fail the requirements of the guideline during stable periods because we did not observe ∆Tu > 8 NTU for more than 24 h. We also discuss the importance of contingency response plans to deal with stochastic environmental events.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), 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".