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Record W312584022

Causes and impacts of northern North Atlantic freshening

2009· dissertation· en· W312584022 on OpenAlexaboutno aff
Markus Scheinert

Bibliographic record

VenueHelmholtz Centre for Ocean Research Kiel (GEOMAR) · 2009
Typedissertation
Languageen
FieldEarth and Planetary Sciences
TopicOceanographic and Atmospheric Processes
Canadian institutionsnot available
Fundersnot available
KeywordsNorth Atlantic Deep WaterOceanographyThermohaline circulationOcean gyreShutdown of thermohaline circulationContext (archaeology)GeologyClimatologyWater massAtlantic Equatorial modeSubtropicsFisheryPaleontology
DOInot available

Abstract

fetched live from OpenAlex

The main focus of this study is on the variability of the freshwater budget in the subpolar North Atlantic. This region plays a crucial role in the large scale ocean circulation since the North Atlantic Deep Water (NADW) is formed here, which is an important part of the Meridional Overturning Circulation (MOC). Repeatedly appearing freshening events in the upper layer, known as 'Great Salinity Anomalies' during the 1970s, 80s and 90s, are hereby suspected to weaken the processes of deep water formation. Besides an increase of freshwater in the upper NADW, which is mainly fed by the Deep Convection in the Labrador Sea, there is also observational evidence for a freshening of the lower NADW. In this context, a large increase of the freshwater content in the subpolar basin and in the Nordic Seas has been found in observations between 1970 and 1995. However, the mechanisms that drive this freshening are still unclear. Numerous studies have investigated the variability of freshwater exports out of the Arctic, but only little is known about the exchange with the Atlantic south of the Subpolar Gyre. This study tries to close the gap for a better understanding of the deep water formation variability and the associated changes in the Meridional Overturning Circulation (MOC).

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.126
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.021
GPT teacher head0.278
Teacher spread0.257 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2009
Admission routes1
Has abstractyes

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