A new high-resolution Coastal Ice-Ocean Prediction System for the East Coast of Canada
Why this work is in the frame
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Bibliographic record
Abstract
Abstract The Coastal Ice Ocean Prediction System for the East Coast of Canada (CIOPS-E) was developed and implemented operationally at Environment and Climate Change Canada (ECCC) to support a variety of critical marine applications. These include support for ice services, search and rescue, environmental emergency response and maritime safety. CIOPS-E uses a 1/36° horizontal grid (~ 2 km) to simulate sea ice and ocean conditions over the northwest Atlantic Ocean and the Gulf of St. Lawrence (GSL). Forcing at lateral open boundaries is taken from ECCC’s data assimilative Regional Ice-Ocean Prediction System (RIOPS). A spectral nudging method is applied offshore to keep mesoscale features consistent with RIOPS. Over the continental shelf and GSL, the CIOPS-E solution is free to evolve according to the model dynamics. Overall, CIOPS-E significantly improves the representation of tidal and sub-tidal water levels compared to ECCC’s lower resolution systems: RIOPS (~ 6 km) and the Regional Marine Prediction System – GSL (RMPS-GSL, 5 km). Improvements in the GSL are due to the higher resolution and a better representation of bathymetry, boundary forcing and dynamics in the upper St. Lawrence Estuary. Sea surface temperatures show persistent summertime cold bias, larger in CIOPS-E than in RIOPS, as the latter is constrained by observations. The seasonal cycle of sea ice extent and volume, unconstrained in CIOPS-E, compares well with observational estimates, RIOPS and RMPS-GSL. A greater number of fine-scale features are found in CIOPS-E with narrow leads and more intense ice convergence zones, compared to both RIOPS and RMPS-GSL.
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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 it