Past Bank Erosion as a Guide for Bank Erosion Prediction at Pipeline Crossings
Bibliographic record
Abstract
This paper presents a methodology which uses past bank erosion behaviour as a predictor of future performance. The methodology employed in the bank erosion study consists of the following main steps: identifying a reach to examine, classifying the watercourse, estimating key hydrotechnical properties, obtaining historical air photographs of the reach, georeferencing or orthorectifying the airphotos, mapping the position of the channel edge, obtaining the historical records of nearby gauges to estimate the return period of floods that have occurred between successive pairs of historical air photographs, and finally combining the results to provide correlations between the rates of bank erosion and the rarity of the floods that have occurred. More than 70 bank erosion studies have been completed in the past two years at a variety of watercourses. This paper provides three case histories that illustrate the methodology and then proceeds to provide some tentative relationships that could be used to focus future bank erosion studies on those sites most active, and used to provide a preliminary estimate of the amount of bank erosion that could be expected in both design settings and existing pipeline integrity evaluations. In this study wandering rivers are more laterally active than other channel pattern types. Although the smallest floods do not cause large-scale changes to the banks, significant bank erosion can be caused by either moderate (20-year) or extreme (100-year) events with a rough trend to larger bank erosion in larger floods. No significant correlation between the time elapsed between successive air photos and the magnitude of erosion was found, suggesting that bank erosion is an event-driven process rather than time dependent.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".