Application of a nested‐grid ocean circulation model to Lunenburg Bay of Nova Scotia: Verification against observations
Bibliographic record
Abstract
A nested‐grid ocean circulation modeling system is used to study the response of Lunenburg Bay in Nova Scotia, Canada, to local wind‐forcing, tides, remotely generated waves, and buoyancy forcing in the summer and fall of 2003. Quantitative comparisons between observations and model results demonstrate that the modeling system reproduces reasonably well the observed sea level, temperature, salinity, and currents in the bay. Numerical results reveal that the spatial and temporal variability of temperature and salinity in the bay during the study period is mainly forced by the local wind stress and surface heat/freshwater fluxes, with some contribution from tidal circulation. In particular, the local heat balance on the monthly timescale is dominated by cooling due to vertical advection and warming due to horizontal advection and net surface heat flux, while high‐frequency variations (timescales of 1–30 days) are mainly associated with vertical advection, i.e., wind‐induced upwelling and downwelling. There is also a strong baroclinic throughflow over the deep water region outside Lunenburg Bay that is strongly influenced by wind‐forcing. The vertically integrated momentum balance analysis indicates a modified geostrophic balance on the monthly timescale and longer, and is dominated by the pressure term and wind minus bottom stress in the high‐frequency band.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".