Marine Infrastructures in Nunavik and Climate Changes
Bibliographic record
Abstract
During the last decade, the fourteen villages in Nunavik, Quebec, were equipped with marine infrastructures (breakwaters, wharves, access ramps). In the context of climate changes, some of these infrastructures may be vulnerable to wave and ice conditions that could overcome their design criteria, mainly based on past data. Transports Quebec, in collaboration with Kativik Regional Government (Inuit local government) and Ouranos, is leading this study trying to assess the impacts of climate changes on environmental variables that may affect northern harbours and to implement adaptation measures accordingly. The project includes data collection in 7 of the 14 villages where vulnerabilities have been anticipated. The data collecting program includes meteorological data collection, water levels recording and sequential ice photography of the harbours and surroundings. These data, in conjunction with Radarsat-II imagery and existing meteorological and tidal data, will be used to validate several numerical models covering Ungava Bay, Hudson Bay and Hudson Strait. It includes a coupled 3D oceanic sea ice model, a 2D storm surge model (both forced by the Canadian regional climate model) and a storm tracking model. The model results and information derived from traditional knowledge from Inuit people will be used to provide indicators and parameters for assessing the changes in storminess, storm surges, wind waves and ice conditions in the vicinity of the infrastructures, according to various future climate scenarios. These results will be used to produce relevant statistics to revise design criteria and, depending on site specific risk assessments, maintenance and rehabilitation programs of the marine infrastructures. The organizations participating in this research project are: Transports Quebec, Kativik Regional Government, Ouranos, INRS-ETE, UQAR-ISMER, Indian and Northern Affairs Canada, Transport Canada, Natural Resources Canada, Environnement Illimite, CIMA+ and Groupeconseil LaSalle.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 teacher head, 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".