Evaluation of the CONCEPTS Sea Ice Forecasts
Bibliographic record
Abstract
With the ever-increasing interest in resource exploitation and marine transport in the Arctic there is a mounting need for improved knowledge about the current and future environmental conditions in the Arctic. This need is being addressed in Canada by a tri-ministerial initiative called the Canadian Operational Network of Coupled Environmental PredicTion Systems (CONCEPTS) among Environment Canada (EC), Fisheries and Oceans Canada (DFO), and the Department of National Defence (DND). CONCEPTS, in close collaboration with the French operational oceanographic centre Mercator-Ocean, is providing a framework for research and operations on coupled atmosphere-ice-ocean prediction in Canada. Operational activity in CONCEPTS is based on coupling the Canadian atmospheric GEM model with the Mercator ice-ocean forecasting system based on the Nucleus for European Modelling of the Ocean (NEMO) ice-ocean model. The Mercator data assimilation system is based on a multi-variate reduced-order Extended Kalman Filter that assimilates sea level anomaly, sea surface temperature (SST) and in situ temperature and salinity data. Using the Mercator forecasting system, weekly 1/4° resolution global 10-day iceocean forecasts are now being produced as well as daily 10-day forecasts at 1/12° resolution for the Northwest Atlantic. Ice fields are initialized using a 3D variational (3DVAR) ice analysis system that assimilates the manual ice analyses from the Canadian Ice Service (CIS), Radarsat manual analyses as well as AMSR-E data. In addition, a high-resolution regional forecasting system for the Arctic is also under development. This system is initialized using 3DVAR ice analyses on a 5km North American grid (including the western Arctic) and produces daily 48hr ice forecasts. Here, the authors provide an overview of these activities, summarize results to date, and discuss plans for new and future operational systems.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".