Freshwater variability in the North Atlantic subpolar gyre from 1993 to 2016 based on ECCO reanalysis data
Bibliographic record
Abstract
This thesis contributes to the investigation of the freshwater (re-)distribution in the North Atlantic in the decades around the turn of the millenium (1993-2016). The examinations are based on ECCO reanalysis data [1–3], which is used to describe the freshwater variability in the subpolar gyre (SPG). Possible driving mechanisms for variability and exchange with the surrounding regions are discussed. The freshwater variability in the subpolar gyre of the North Atlantic can be divided into three periods in the investigated time intervall (1993-2016). The first period (until 2000) shows high variability and an overall salinity increase. During the 2000s, the freshwater content stagnates. Since about 2009, the salinity decreases, while again an intensified variability is found. This is peaked by an extreme freshening event in 2011. Over the total period from 1993 to 2016, the whole subpolar North Atlantic is freshening. The deep layers (below 200m) seem to be isolated from the freshwater variability in the upper ocean layers. Diffusive and turbulent freshwater fluxes are found to be the main driver of mixed layer variability, while advective transports determine the freshwater content in the deep layers of the SPG. Impacts of the total freshwater surface fluxes are found in the western SPG, whereas precipitation has a negligible contribution. The surface freshwater entries are transported via the Labrador Current and the East Greenland Current and enter the SPG at the Canadian coast. The transport inside the gyre is mainly from the Canadian coast in northeast direction.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".