Bibliographic record
Abstract
Abstract. We explore historical variability in the volume of Labrador Sea Water (LSW) using ECCO, an ocean state estimate configuration of the Massachusetts Institute of Technology general circulation model (MITgcm). The model’s adjoint, a linearization of the MITgcm, is set up to output the lagged sensitivity of the watermass volume to surface boundary conditions. This allows us to reconstruct the evolution of LSW volume over recent decades using historical surface wind stress, heat, and freshwater fluxes. Each of these boundary conditions contributes significantly to the LSW variability that we recover, but these impacts are associated with different geographical fingerprints and arise over a range of time lags. We show that the volume of LSW accumulated in the Labrador Sea exhibits a delayed response to surface wind stress and buoyancy forcing outside the convective interior of the Labrador Sea, at key locations in the North Atlantic Ocean. In particular, winds and surface density anomalies affect the North Atlantic Current’s (NAC) transport of warm and saline subtropical water masses that are precursors for the formation of LSW. This propensity for a delayed response of LSW to remote forcing allows us to predict a substantial fraction of LSW variability at least a year into the future. Our analysis also enables us to attribute LSW variability to different boundary conditions and to gain insight into the major mechanisms that drive volume anomalies in this deep watermass. We point out the important role of buoyancy loss and preconditioning along the NAC pathway, in the Iceland Basin, the Irminger Sea, and the Nordic Seas, processes which facilitate the formation of LSW both in the Irminger and in the Labrador Sea.
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 | 0.004 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.029 | 0.032 |
| Insufficient payload (model declined to judge) | 0.084 | 0.064 |
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".