Variability in formation, properties, and transport of North Atlantic Deep Water
Bibliographic record
Abstract
North Atlantic Deep Water is found in much of the deep Atlantic Ocean, and its formationin the Labrador and Nordic Seas and subsequent southward export are a vital part of globalocean circulation and Earth’s climate system. The overarching goal of this dissertation is tobetter understand the processes controlling variability of North Atlantic Deep Water formation,properties, and transport in the Atlantic Ocean.Chapter 1 uses data from the central Labrador Sea during winter to estimate the uptake of oxygenassociated with deep convection in 2014–15. The results show that intense air-sea exchangeresults in an uptake of 29.1 ± 3.8 mol m^−2 during the convective season, with much of the fluxbeing associated with injection of air bubbles. Chapter 2 looks at lateral fluxes of carbon, oxygen,and nitrate from the Labrador Sea’s boundary current into the center of the basin during thesummertime productive season. Lateral fluxes are found to play an important role for the carbonand nitrate budgets immediately below the mixed layer, with respiration rates underestimated byup to 50% if they are ignored.In chapter 3, gravity measurements from satellites are used to investigate variability in oceancirculation. After trends in the data are validated using independent measurements, they are usedto study decadal circulation changes of North Atlantic Deep Water in the North Atlantic Ocean.The analysis reveals a strengthening of the interior branch of North Atlantic Deep Water flow,with transport increasing by 13.9 ± 3.7 Sv (1 Sv = 10^6 m^3 s^−1 ), balanced by a weaker southwardflow in the Deep Western Boundary Current.A twenty-year record of mooring data is analyzed in chapter 4 to investigate changes in NorthAtlantic Deep Water transport at 16 ◦ N. Multi-decadal variability is observed in the transport timeseries, and is largely associated with density changes in the lower half of the North Atlantic DeepWater layer, which in turn appear to be caused by changes in the source region. The data are alsocompared to another transport time series at 26 ◦ N, and similarities and differences are discussed.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".