HYDROMORPHOLOGICAL DYNAMICS OF CANADIAN ARCTIC DELTAS : AN HYDROLOGICAL MODEL OF THE COPPERMINE DELTA
Bibliographic record
Abstract
Climate change has transformed coastal and deltaic environments in the Arctic with warmer temperatures, loss of sea ice, higher water levels, and larger storm events. Coastal retreat rates have been measured over 10 m per year in Canadian Arctic severely affecting Indigenous communities. This paper presents one of the first hydrodynamic models of an Arctic delta to better understand the processes specific to the Arctic. The hydrodynamic model has been calibrated and validated with water level and current velocity field measurements in summer 2022. Water levels are well predicted during both calm weather and storm events. A correlation between water level and permafrost erosion at cliff foot is analyzed with a parameter called erosion potential. The erosion potential has been defined as the number of days for which the water level at Graveyard is above the threshold of 0.1 m divided by the total number of days during summer. According to water level measurements from 2021 to 2024, the erosion potential is stochastic across years, with a significant increase in mean water level in 2023, followed by a drop in 2024. Following IPCC projections for sea-level rise, erosion at the foot of Graveyard cliff could occur one out of two days during the summer season in 2050 and every day in 2100. Based on our recent field studies, the aim of this project is to analyze the extent to which hydro- morphodynamic models without considering thermal exchanges can be applied to Arctic coastal engineering. Correctly predicting the impact of climate change in the Arctic will enable better adaptation of local communities in the future.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".