Modelling the effects of climate and flow regulation on ice‐affected backwater staging in a large northern river
Bibliographic record
Abstract
Abstract In cold region environments, ice‐jam floods (IJFs) pose a severe risk to local communities, economies, and ecosystems. Previous studies have shown that both climate and regulation affect IJF probabilities, but their relative impacts are poorly understood. This study presents a probabilistic modelling framework that couples hydrologic and hydraulic models to assess the relative role of regulated and naturalized flows on ice‐affected backwater staging. The framework is evaluated at an IJF‐prone town on the Peace River in western Canada, which has been regulated since 1972. Naturalized flows were generated for the comparison, and ice‐affected backwater profiles were calculated along jams of varying length and location and for different combinations of model parameters and boundary conditions. Results show significant differences in backwater staging (~2 m for a return period of T = 1:10 year) between two study time periods (1973–1992 vs 1993–2012) as compared with two different hydraulic flow conditions (regulated vs naturalized), suggesting a larger role of climate than regulation in backwater staging. However, regulation was found to offset flood risk during the 1973–1992 period and exacerbate flood risk during the 1993–2012 period.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".