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Record W4321502233 · doi:10.5194/egusphere-egu23-4077

Interannual variability of the meridional overturning circulation in the subpolar North Atlantic

2023· preprint· en· W4321502233 on OpenAlexaboutno aff
Yao Fu, M. Susan Lozier

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsnot available
Fundersnot available
KeywordsZonal and meridionalThermohaline circulationClimatologyOceanographyNorth Atlantic Deep WaterShutdown of thermohaline circulationStructural basinCirculation (fluid dynamics)Water massGeologyPaleontology

Abstract

fetched live from OpenAlex

Variability of the Atlantic Meridional Overturning Circulation (MOC) has drawn extensive attention due to the MOC’s impact on global heat and freshwater redistribution. The Overturning in the Subpolar North Atlantic Program (OSNAP) array, consisting of an OSNAP West section covering the Labrador Sea and an OSNAP East section covering the eastern subpolar basins (Irminger and Iceland Basins), has continuously observed the MOC and meridional heat and freshwater transports since 2014. The OSNAP observations have contributed substantially to the understanding of the mean state and sub-seasonal to seasonal variability of the subpolar MOC. In this study, we present the latest OSNAP observational results and investigate interannual variability of the subpolar MOC with respect to water mass transformation and formation in the Labrador Sea and eastern subpolar basins. We detail the differences between formation and transformation in each of these basins and discuss their relationships to overturning on monthly and interannual time scales. Finally, we explore the mechanism(s) responsible for these differences.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.037
GPT teacher head0.260
Teacher spread0.223 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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
Published2023
Admission routes1
Has abstractyes

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