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Record W3105203633 · doi:10.1029/2020gl089764

A New Mass Flux Correction Procedure for Vertically Integrated Energy Transport by Constraining Mass, Energy, and Water Budgets

2020· article· en· W3105203633 on OpenAlexfundno aff
Hamza Kunhu Bangalath, Olivier Pauluis

Bibliographic record

VenueGeophysical Research Letters · 2020
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOceanographic and Atmospheric Processes
Canadian institutionsnot available
FundersYork UniversityNew York University Abu Dhabi
KeywordsSpurious relationshipBarotropic fluidMass fluxEnergy transportFlux (metallurgy)Environmental scienceEnergy (signal processing)Water massEnergy fluxMass transportEnergy budgetEnergy transformationZonal and meridionalCirculation (fluid dynamics)MechanicsPhysicsMeteorologyClimatologyAtmospheric sciencesGeologyMaterials scienceMathematicsThermodynamicsStatistics

Abstract

fetched live from OpenAlex

Abstract Reconstruction of the atmospheric circulation from the numerical output of general circulation model (GCM) and reanalysis products often suffers from spurious values resulting in imbalances in the mass, energy, and water transport. A correction hence is needed prior to the analysis of energy transport. Most of the previous correction methods only employ a mass budget adjustment since it is the primary source of error. The present study proposes a new method of mass flux correction by constraining the mass, energy, and water budgets that can be applied to meridional energy transport and overturning circulation. The new method seeks a vertical dependent solution, unlike the conventional barotropic correction procedures. The procedure has been tested for one aquaplanet GCM and a reanalysis product. The method successfully corrects the spurious imbalances in the mean meridional energy transport and circulation.

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)0.001
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: Methods · Consensus signal: Methods
Teacher disagreement score0.009
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.016
GPT teacher head0.229
Teacher spread0.213 · 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
GenreMethods

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

Citations2
Published2020
Admission routes1
Has abstractyes

Explore more

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