Modeling ocean and sea ice dynamics of the Canadian Arctic Archipelago: Aspects of forcing
Bibliographic record
Abstract
The Canadian Arctic Archipelago (CAA) is one of the main pathways for freshwater exiting the Arctic Ocean. \nFreshwater exported to the North Atlantic may influence the deep water formation in the Labrador Sea, and thus \nthe meridional overturning circulation. Modeling ocean and sea ice conditions of the CAA is difficult because of narrow straits and complex coastlines. The Finite-Element Sea-ice Ocean circulation Model (FESOM) configured on a global mesh is applied to assess the volume, freshwater and sea ice transports through the CAA.With a mesh resolution of 5 km in the CAA we are able to accurately resolve complex coastlines. Outside the CAA the mesh is refined to 24 km north of 55N with a global background resolution of 1.5degrees. In this study, first, it is shown that the transports modeled with FESOM correlate well with the available observational data. Second, the model is used to learn about the impact of different atmospheric forcing datasets differing in spatial and temporal resolution (CORE 2 and the Reforecast dataset from Environment Canada). The CORE 2 dataset is on the T62 grid, which is coarse compared to the Reforecast dataset with grid resolution of 0.45degrees longitude and 0.3degrees latitude. The temporal resolution of the Reforecast dataset is higher than the CORE 2 dataset (one hourly and 6-hourly data, respectively, for wind, surface temperature and specific humidity fields). The representation of sea ice in the CAA can be improved by using the high resolution atmospheric forcing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".