Bank erosion processes within the fluvial corridor of the St. Lawrence River: causes, drivers and future challenges.
Bibliographic record
Abstract
The St. Lawrence River is one of the largest river affected by seasonal ice formation outside the periglacial domain. It is also a major socio-economical axis in eastern North America where human activities and facilities are numerous within its fluvial corridor, upstream Quebec City (QC, Canada). Recent flooding episodes in 2017 and 2019 have shown that the sustainability of the St. Lawrence River and thus its socio-economic and ecological services will likely be altered in a near future. Bank erosion today represents a major hazard for land owners, infrastructures and riverine ecosystems. Consequently, there is a growing need to integrate bank erosion hazard in order to ensure sustainable management of riparian areas and to adapt land-use planning strategies. Literature review and field surveys allowed us to conceptualize a scientific framework about bank erosion dynamics in large rivers characterized by the formation of river ice. We used this framework within the fluvial corridor of the St. Lawrence River to identify bank erosion processes and their drivers, with a focus on the role of river ice and the impacts of anthropogenic stressors such as urbanization, riverbank concreting, large-scale damming, and maritime traffic. We illustrate erosion processes and their impacts through several case studies representing different ecosystems from the fluvial section of the St. Lawrence River. We then discuss the future changes in the nature, the timing, the frequency and the magnitude of bank erosion processes to address the challenges caused by climate change and increased human activities in the St. Lawrence, and more generally in large rivers affected by seasonal ice formation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".