MétaCan
Menu
Back to cohort
Record W2114933831 · doi:10.1175/jcli-d-11-00040.1

On Challenges in Predicting Bottom Water Transport in the Southern Ocean

2011· article· en· W2114933831 on OpenAlexaff
Oleg A. Saenko, Alex Sen Gupta, Paul Spence

Bibliographic record

VenueJournal of Climate · 2011
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOceanographic and Atmospheric Processes
Canadian institutionsEnvironment and Climate Change Canada
FundersOffice of ScienceLawrence Livermore National LaboratoryU.S. Department of Energy
KeywordsAntarctic Bottom WaterWind stressClimatologyGeologyOcean currentClimate modelWesterliesOceanographyClimate changeAtmospheric sciencesEnvironmental scienceThermohaline circulation

Abstract

fetched live from OpenAlex

Abstract Changes in the Southern Ocean lower-limb overturning circulation are analyzed using a set of climate models. In agreement with some recently developed theoretical models, it is found that the overturning can be strongly affected by winds. In particular, the simulated strengthening of large-scale southward transport in the abyss is explicitly driven by zonal wind stress. However, there is a considerable range among the climate models in their projected changes of Southern Ocean wind stress. Furthermore, the strengthening of large-scale southward transport tends to be compensated by eddy-induced northward flows in the abyss, particularly at eddy-permitting resolution. As a result, the net Antarctic Bottom Water (AABW) export may only be weakly affected. However, none of the models considered accounts for the possibility that a fraction of the eddy kinetic energy may be converted to diapycnal mixing. If this were the case, the presented energetic arguments suggest that stronger Southern Ocean winds would result in a stronger AABW transport.

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)0.002
metaresearch head score (Gemma)0.004
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.029
Threshold uncertainty score0.059

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.030
GPT teacher head0.208
Teacher spread0.179 · 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

Citations12
Published2011
Admission routes1
Has abstractyes

Explore more

Same venueJournal of ClimateSame topicOceanographic and Atmospheric ProcessesFrench-language works237,207