Impact of Different Atmospheric Forcing Sets on Modeling Labrador Sea Water Production
Bibliographic record
Abstract
Abstract A numerical modeling sensitivity study is carried out within the Labrador Sea by varying the atmospheric conditions. From forcing NEMO simulations with five atmospheric products commonly used in ocean modeling (DFS5.2, ERA‐Interim, CGRF, ERA5, and JRA55‐do), we calculate the air–sea heat fluxes that occur over the Labrador Sea (2002–2015 annual‐average net heat flux: −53.4, −51.0, −46.6, −58.5, and −47.9 W m−2). With differences up to 12 W m−2 in net surface heat flux averaged over a central region of the Labrador Sea, each product supplied different atmospheric conditions. While the salinity‐dependent surface buoyancy fluxes were similar across all simulation, differences between each simulation's solar and nonsolar heat flux led to significant changes in the level of stratification (up to 400 J m−3), depth of the mixed layer (up to 300 m), and thickness of Labrador Sea Water (LSW; up to 300 m). Greater buoyancy loss from the Labrador Sea produced LSW with greater density. However, the production rate of LSW was not clearly affected by small changes in the surface buoyancy flux.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".