A high-resolution ocean and sea-ice modelling system for the Arctic and North Atlantic Oceans
Bibliographic record
Abstract
Abstract. As part of the CONCEPTS (Canadian Operational Network of Coupled Environmental PredicTion Systems) initiative, The Government of Canada is developing a high resolution (1/12°) ice–ocean regional model covering the North Atlantic and the Arctic oceans. The objective is to provide Canada with short-term ice–ocean predictions and hazard warnings in ice infested regions. To evaluate the modelling component (as opposed to the analysis – or data-assimilation – component), a series of hindcasts for the period 2003–2009 is carried out, forced at the surface by the Canadian Global Re-Forecasts. These hindcasts test how the model represent upper ocean characteristics and ice cover. Each hindcast implements a new aspect of the modelling or the ice–ocean coupling. Notably, the coupling to the multi-category ice model CICE is tested. The hindcast solutions are then assessed using a validation package under development, including in-situ and satellite ice and ocean observations. The conclusions are: (1) the model reproduces reasonably well the time mean, variance and skewness of sea surface height. (2) The model biases in temperature and salinity show that while the mean properties follow expectations, the Pacific Water signature in the Beaufort Sea is weaker than observed. (3) However, the modelled freshwater content of the Arctic agrees well with observational estimates. (4) The distribution and volume of the sea ice is shown to be improved in the latest hindcast thanks to modifications to the drag coefficients and to some degree as well to the ice thickness distribution available in CICE. (5) On the other hand, the model overestimates the ice drift and ice thickness in the Beaufort Gyre.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".