Kinematic Subduction Rate Of Labrador Sea Water From an Eddy‐Permitting Numerical Model
Bibliographic record
Abstract
Abstract We use an eddy‐permitting, 1/12° regional configuration of the Nucleus for European Modelling of the Ocean (NEMO) model to examine water mass subduction rates in the Labrador Sea for the 2002–2013 period. An instantaneous kinematic subduction approach is implemented to calculate the subduction rate of Labrador Sea Water (LSW). By following the outcrop positions of a given isopycncal range, we calculate the vertical transport of a water mass from the mixed layer into the permanent thermocline over the course of a year. We examine the importance of the various terms in this approach, including the evolution of the Mixed Layer Depth (MLD), the advection across the base of the Mixed Layer (ML), and the vertical velocity at the base of the ML. We find that the subduction rate is not greatly affected by the definition of the MLD, as long as the integration time is long enough for the subduction‐obduction processes to balance each other. The total LSW subduction rate is ∼4–5 Sv, with similar rates for both Upper (ULSW) and Classical LSWs (CLSW), (∼2–2.5 Sv). After 2008, a shift in the LSW density is found in the simulation. CLSW reaches a maximum rate of 6 Sv in 2008, which is mainly inferred by the instantaneous ML change.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".