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Record W2024297382 · doi:10.1016/j.ocemod.2014.03.004

An assessment of global and regional sea level for years 1993–2007 in a suite of interannual CORE-II simulations

2014· article· en· W2024297382 on OpenAlexaff
Stephen M. Griffies, Jianjun Yin, Paul J. Durack, Paul B Goddard, Susan C. Bates, Erik Behrens, Mats Bentsen, Daohua Bi, Arne Biastoch, Claus W. Böning, Alexandra Bozec, Eric P. Chassignet, Gökhan Danabasoglu, Sergey Danilov, Catia M. Domingues, Helge Drange, Riccardo Farneti, Elodie Fernandez, Richard J. Greatbatch, David M. Holland, Mehmet Ilıcak, William G. Large, Katja Lorbacher, Jianhua Lü, Simon Marsland, Akhilesh Mishra, A. J. George Nurser, David Salas y Mélia, Jaime B. Palter, Bonita L. Samuels, Jens Schröter, Franziska U. Schwarzkopf, Dmitry Sidorenko, Anne‐Marie Tréguier, Yu‐Heng Tseng, Hiroyuki Tsujino, Petteri Uotila, Sophie Valcke, Aurore Voldoire, Qiang Wang, Michael Winton, Xuebin Zhang

Bibliographic record

VenueOcean Modelling · 2014
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOceanographic and Atmospheric Processes
Canadian institutionsMcGill University
FundersLawrence Livermore National LaboratoryNatural Environment Research CouncilGoddard Space Flight CenterOffice of ScienceCentre National de la Recherche ScientifiqueU.S. Department of EnergyEuropean CommissionNational Oceanic and Atmospheric AdministrationSight Research UKDepartment of the Environment, Australian GovernmentNew York University Abu DhabiCommonwealth Scientific and Industrial Research OrganisationAustralian GovernmentNational Science Foundation
KeywordsOcean gyreClimatologyEnvironmental scienceSea levelHydrographyArcticOcean heat contentAdvectionSea iceOceanographyGeologySea surface temperatureSubtropics

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.003
metaresearch head score (Gemma)0.005
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 score0.107
Threshold uncertainty score0.213

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
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 head0.043
GPT teacher head0.300
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 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

Citations140
Published2014
Admission routes1
Has abstractno

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