Findings from a National Survey of Canadian perspectives on predicting river channel migration and river bank erosion
Bibliographic record
Abstract
Abstract River bank erosion and river channel migration are geomorphic processes that can result in significant hazards when there are impacts to humans or infrastructure. Unlike flooding, there are limited national guidelines in Canada that provide recommendations on how to best assess riverine erosion hazards. Instead regional and local jurisdictions rely on techniques based on varying levels of policy maturity. The current study presents findings of a nationwide survey on Canadian perspectives on predicting river channel migration and river bank erosion which received more than 40 responses from across Canada. Results showed that predictions were used for a variety of purposes, but that confidence intervals were rarely reported. Aerial imagery and survey‐based methods were the well‐known and widely‐used techniques for predicting river channel migration and river bank erosion. A majority of respondents identified both technical and financial challenges to improving accuracy including client willingness to pay, data quality/cost issues, and hydrologic changes due to land use and climate change. Several recommendations for improving best‐practices are provided, with a focus on the development of erosion datasets, improving data access, and providing additional training opportunities.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".