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Closing the loops on Southern Ocean dynamics: From the circumpolar current to ice shelves and from bottom mixing to surface waves

2023· preprint· en· W4383621688 on OpenAlexaff
Luke G. Bennetts, Callum J. Shakespeare, Catherine A. Vreugdenhil, Annie Foppert, Bishakhdatta Gayen, Amélie Meyer, Adele K. Morrison, Laurie Padman, Helen E. Phillips, Craig Stevens, Alessandro Toffoli, Navid C. Constantinou, Geoffrey J. Stanley

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEarth and Planetary Sciences
TopicOceanographic and Atmospheric Processes
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsCircumpolar starOcean currentOceanographyContext (archaeology)Earth system scienceClimatologyGeologyCurrent (fluid)GeographyTop-down and bottom-up designSea iceComputer science

Abstract

fetched live from OpenAlex

A holistic review is given of the Southern Ocean dynamic system, in the context of the crucial role it plays in the global climate and the profound changes it is experiencing. The review focuses on connections between different components of the Southern Ocean dynamic system, drawing together contemporary perspectives from different research communities, with the objective of ‘closing loops’ in our understanding of the complex network of feedbacks in the overall system. For the purposes of this review, the Southern Ocean dynamic system is divided into four main components: large-scale circulation; cryosphere; turbulence; and gravity waves. Overviews are given of the key dynamical phenomena for each component, before describing the linkages between the components. The reviews are complemented by an overview of observed Southern Ocean trends and future climate projections. Priority research areas required to improve our understanding of the Southern Ocean system are identified.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.024
GPT teacher head0.233
Teacher spread0.210 · 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 designSimulation or modeling
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

Citations9
Published2023
Admission routes1
Has abstractyes

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