Evaluating the Impacts of Climate Changes on Nunavik Marine Infrastructures and Adaptation Solutions
Bibliographic record
Abstract
Since 1999, fourteen marine infrastructures have been built in the northern villages of Nunavik, Quebec. These infrastructures generally consist in breakwaters, concrete wharves, access ramps and floating pontoons. During the design of these infrastructures, no consideration was given to potential climate change influence on specific design parameters. Neither data nor projections were available at the time. Transports Quebec, in collaboration with Kativik Regional Government, Ouranos and CIMA+ then developed a project aiming at evaluating the marine infrastructure vulnerability in a context of climate change in order to identify adaptation solutions and ensure these infrastructures will last and stay safe to use. In order to anticipate potential impacts of climate changes on marine infrastructures, storm tracks, water levels, waves, sea and coastal ice are being modelled in the Nunavik region. Climate change will impact the way storms travel. Storms can occasionally produce large waves and severe surges that both may impact the infrastructures in several ways. The longer ice-free season increases the probability for waves and storm surges to develop. The reduced stability of the ice cover increases the risk of ice-related damage on the marine infrastructures. Different models (sea ice, storm surge, waves) are being tested and validated in order to check the influence of the different storm track patterns and the run. Integration of all this information in a comprehensive marine infrastructure vulnerability evaluation model will definitely be a challenge, but the first results are quite promising.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".