Exploring the role of coastal structures in shaping cold‐temperate river–estuarine marsh environments
Bibliographic record
Abstract
Abstract Fine sediment environments in estuaries, shaped by the dynamic equilibrium between geomorphic and ecological processes, provide valuable ecosystem services. Previous studies have shown that anthropogenic structures like jetties and protective walls disrupt sediment transport and flow patterns, exacerbating ecosystem instability, particularly under high‐intensity hydrometeorological events. However, the nonlinear evolution of fine sedimentary systems, such as intertidal marshes, makes it challenging to differentiate the implications of seasonally varying natural processes from long‐standing anthropogenic modifications. This study aims to evaluate the influence of coast‐parallel and perpendicular‐to‐coast structures on sediment dynamics and coastline evolution in a cold‐temperate river‐estuarine setting. High‐resolution digital surface models (DSMs) were generated using unmanned aerial system (UAS) photogrammetric surveys conducted over 4 years for comparing two contrasting coastal sectors, composed of mixed marsh and beach systems located within the fluvio‐tidal transition zone of the St. Lawrence Fluvial Estuary (SLFE). These DSMs were analysed alongside historical coastline positions, modelled wave data, atmospheric temperatures, water level records, and archival documentation of human interventions to assess seasonal and geomorphic changes over the past 70 years. The results highlight that shorter perpendicular structures potentially promote fine sediment deposition and colonisation from pioneer marsh vegetation, leading to marsh creation. In contrast, longer structures can trigger a positive feedback loop resulting in decreasing elevation of marsh surfaces. The magnitude of geomorphic changes in the SLFE is primarily linked to the strong seasonality behind fluvial and landfast ice processes, rather than storm events. While human structures can amplify or dampen natural dynamics, these results illustrate how integrated and adaptable designs can enhance marsh development, resilience and sustainability.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".