Performance Assessment of a Coupled Circulation–Wave Modelling System for the Northwest Atlantic
Bibliographic record
Abstract
We present a modified version of a coupled circulation–wave modelling system for the northwest Atlantic (CWMS-NWA) by including additional physics associated with wave–current interactions. The latest modifications include a parameterization of Langmuir turbulence and surface flux of turbulent kinetic energy from wave breaking in vertical mixing. The performance of the modified version of CWMS-NWA during Hurricane Arthur in 2014 is assessed using in situ measurements and satellite data. Several error statistics are used to evaluate the model performance, including correlation (R), root mean square error (RMSE), normalized model variance of model errors (γ2) and relative bias (RB). It is found that the simulated surface waves (R ≈ 94.0%, RMSE ≈ 27.5 cm, γ2≈ 0.16) and surface elevations (R ≈ 97.3%, RMSE ≈ 24.0 cm, γ2≈ 0.07) are in a good agreement with observations. The large-scale circulation, hydrography and associated storm-induced changes in the upper ocean during Arthur are reproduced satisfactorily by the modified version of CWMS-NWA. Relative to satellite observations of the daily averaged sea surface temperature (SST), the model reproduces large-scale features as demonstrated by the error metrics: R ≈ 97.8%, RMSE ≈ 1.6 °C and RB ≈ 8.6 × 10−3°C.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".