River morphology responses and riparian canopy loss: two case studies from a high-intensity storm in Atlantic Canada
Bibliographic record
Abstract
Abstract As climate change contributes to higher intensity storms, riparian canopy loss may have geomorphic and ecological consequences on streambank sediment dynamics. This study investigates changes of riparian canopy cover, river morphology responses, and hydrological conditions, following an extratropical cyclone in Atlantic Canada. Using LiDAR data acquired from three timesteps (2013, 2019 and 2024), the spatiotemporal changes in canopy cover and streambank surfaces along a ∼5,100 m segment of two rivers were assessed. The Portapique and Great Village watersheds provide critical habitats for the endangered Inner Bay of Fundy (iBoF) Atlantic salmon and further research is required to support recovering populations. Both rivers experienced canopy cover declines of 19% between 2019 and 2024, following a period of canopy growth—likely driven by Hurricane Fiona in 2022. We applied a DTM of Difference (DoD) technique to investigate streambank changes and found aggradation increased by an average of 91.5% during the 2019–2024 period. Historical hydrometric data was used as reference data to understand how streambank morphology may respond differently to peak flow under canopy loss, compared to a period of growth (2013–2019). Our approach provides a novel framework for assessing climate-driven riparian changes through statistical and spatial analysis to support adaptive planning for salmon recovery under increasing storm intensities. A morphodynamic model was also developed as a proof-of-concept to simulate river morphology responses under peak flow conditions, providing a template for future research where topo-bathymetric data is available.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".