Evaluation of climate change impacts on storm surge and water levels - St. Lawrence Marine Corridor climate risk information system
Bibliographic record
Abstract
The impacts of climate change on maritime shipping and transportation infrastructure in the St. Lawrence Marine Transportation Corridor are driven by climate-sensitive factors such as water levels, storm surges, winds, waves, and ice conditions. This study assesses the future changes in these parameters and their effects on the corridor's operation. Despite existing evidence of changing conditions, reliable predictions for the region's ice season duration, severity, storm surges, wind patterns, wave conditions, and water levels are lacking. This knowledge gap hinders the assessment of climate risks and adaptation planning for numerous ports and docks along the Canadian and US coasts of the Great Lakes - St. Lawrence region. The study aims to understand the influence of climate change on the corridor, focusing on water levels, storm surges, and waves. It employs a combination of literature review, data analysis, and numerical simulations to investigate the historical and potential future impacts of various atmospheric, fluvial, and oceanic variables. Conclusions highlight the anticipated changes in river discharge, sea level rise, storm frequencies, wind patterns, wave heights, and ice jam events due to climate change. The findings emphasize the need for tailored strategies to mitigate climate-related risks to coastal infrastructure in the corridor.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".