Status and prospects for the neXtSIM-F CMEMS operational forecast
Bibliographic record
Abstract
The neXtSIM-F operational forecast was upgraded in December 2021, with the following developments: improvements to the rheology, with the neXtSIM model now running the latest version of the Brittle Bingham-Maxwell rheology (BBM). The previous version was running a preliminary version of the BBM rheology. The model domain was extended to include the Labrador Sea, Hudson and Baffin Bay. Better tuning of dynamic (eg of basal stress parameters for the fast ice off the coast of the eastern Arctic) and thermodynamic parameters. The upgrade resulted in good improvements to the ice thickness and extent, although drift developed a slight slow bias. However the bias is of the order of the observation error (1-1.25km/day). Planned developments for the next 3 years include: assimilation of ice thickness data assimilation of ice extent from NIC ice charts (National Ice Center, USA) instead of from passive microwave (OSISAF). increased resolution, to go from about 7.5km to about 3.75km a multi-year reanalysis to be updated every month
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.034 | 0.011 |
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".