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Record W7018036711

Characterizing mesoscale eddies in the Labrador Sea based on data from moored observations

2023· dissertation· en· W7018036711 on OpenAlexaboutno aff

Bibliographic record

VenueHelmholtz Centre for Ocean Research Kiel (GEOMAR) · 2023
Typedissertation
Languageen
FieldEarth and Planetary Sciences
TopicOceanographic and Atmospheric Processes
Canadian institutionsnot available
Fundersnot available
KeywordsEddyMesoscale meteorologyAnticycloneBoundary currentCurrent (fluid)ConvectionWater massVortexOcean current
DOInot available

Abstract

fetched live from OpenAlex

In the Labrador Sea, mesoscale eddies have been identified as a major exchange agent between the fast flowing boundary currents and the quiescent interior Labrador Sea. This way, the eddies contribute to heat, freshwater and property fluxes and impact deep convection and carbon uptake. It is therefore of interest to carefully analyse the occurrence,
\ndynamics and water mass characteristics of mesoscale eddies in the Labrador Sea. Here, four years of moored instrument time series data are analyzed for eddy occurrences.
\nA semi-automatic method for eddy detection in moored velocity data was developed and the eddie’s time series data were fit to a Rankine vortex model in order to estimate eddy characteristics. Over the four years, three cyclonic and seven anticyclonic eddies have been detected with this method. Surprisingly, most eddies differ in their characteristics and structures from the eddies reported in earlier studies, namely Irminger Rings, Convective Eddies, and Boundary Current Eddies. In particular, no Irminger Ring was found but a cyclonic, bottom-intensified warm core eddy, which has not been reported in this area before.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.381
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.124
GPT teacher head0.333
Teacher spread0.209 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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