A conceptual framework for the study of bank erosion in large rivers: insight from the St. Lawrence River, Canada
Bibliographic record
Abstract
The St. Lawrence River forms a major socio-economic and commercial axis in eastern North America, and one of the most populated cold rivers worldwide. Its upstream section, the fluvial corridor, located between the Quebec-Ontario border and Quebec City (QC, Canada), concentrates most of the human facilities and activities. Over the past decades, climate change and increased human pressures on the banks have deeply altered the natural hydro-sedimentary dynamics within this corridor. Yet, the environmental and socio-economic consequences have long been overlooked. The recent flooding events of 2017 and 2019 have highlighted the vulnerability of river habitats and infrastructures, underlining the need to integrate bank erosion hazard to ensure sustainable management of riparian areas and to adapt land-use planning strategies. A research program started in late 2017 allowed a cross-science collaboration between academic scientists, the Government of Québec, environmental organizations and agencies, municipalities as well as concerned citizens. This program aims at characterizing riverbank types within the fluvial corridor and at assessing erosion hazard, processes, and controlling factors over more than 4000 km of riverbanks. We combined field surveys, remote sensing (LiDAR, aerial photographs) and crowdsourcing data to develop a geospatial database for local stakeholders. We present the conceptual framework, methods and main results for riverbanks classification and erosion hazard assessment, and discuss the challenges posed by the study of large rivers. We emphasize on issues related to anthropogenic stressors and specificities (river ice, maritime traffic) of the St. Lawrence River. This is the first time, to our knowledge, that the subject of bank erosion has been assessed with such high resolution, but also with the same method, on an ice-affected river as large and diverse as the St. Lawrence.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.013 |
| Science and technology studies | 0.010 | 0.013 |
| Scholarly communication | 0.014 | 0.004 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".