Bibliographic record
Abstract
In order to study and understand the behavior of sea ice, numerical sea ice models have been developed since the early seventies and have traditionally been based on structured grids and finite difference schemes. This doctoral research is part of the Second-generation Louvain-la-Neuve Ice-ocean Model (SLIM) project whose objective is to bring to oceanography modern numerical techniques. The motivation for this thesis is therefore to investigate the potential of finite element methods and unstructured meshes for sea ice modeling.\nThe Canadian Arctic Archipelago (CAA) is a complex area formed by numerous islands and coastlines and constitutes a nice application for unstructured meshes. Our model is the first to investigate the effects of resolving the CAA on the ice cover features and the importance of the CAA in terms of mass balance is highlighted.\nWe further develop a Lagrangian and adaptive version of the model allowing the computational grid to move with the ice. We take advantage of the locality of the mesh adaptation procedure to update the discontinuous fields thanks to a local Galerkin projection.\nSea ice age patterns and how they change in time provide an integrated view of the recent evolution of sea ice growth, melt and circulation. We first study the vertical age profile in sea ice and analyze the age-thickness relationship in a stand-alone thermodynamic sea ice model of the Arctic. We then take advantage of the Lagrangian model to reproduce the algorithm used to compute satellite retrievals of ice age and compare with different ice age definitions. Several characteristics consistent with satellite observations are deduced from our numerical simulations.
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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) | 0.000 | 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".