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Record W4382657717 · doi:10.1029/2023jc019972

Dual Wave Energy Sources for the Atlantic Niño Events Identified by Wave Energy Flux in Case Studies

2023· article· en· W4382657717 on OpenAlexaff
Qingyang Song, Youmin Tang, Hidenori Aiki

Bibliographic record

VenueJournal of Geophysical Research Oceans · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate variability and models
Canadian institutionsUniversity of Northern British Columbia
FundersYoung Scientists FundNatural Science Foundation for Young Scientists of Shanxi ProvinceChina Postdoctoral Science FoundationPostdoctoral Research Foundation of ChinaNational Natural Science Foundation of China
KeywordsThermoclineClimatologyForcing (mathematics)Rossby waveGeologyKelvin waveEnergy fluxEquatorial wavesFlux (metallurgy)GeophysicsAtmospheric sciencesEnvironmental scienceOceanographyPhysicsEquatorLatitudeGeodesyChemistry

Abstract

fetched live from OpenAlex

Abstract The evolution of equatorial sea surface temperature anomalies during Atlantic Niño events has demonstrated its diversity in both intensity and timing. To investigate the mechanism responsible for this diversity, this study focuses on ocean responses to atmospheric forcing, manipulating the wind forcing in both equatorial and off‐equatorial regions to excite linear ocean models for three types of events that occurred in 1999, 2019, and 2021 respectively. The results reveal the dual wave energy sources for the equatorial Kelvin waves (KWs): one is the local wind forcing in the western tropical basin; the other is the reflection due to the off‐equatorial Rossby waves in the western boundary. The reflected KWs can precondition the events when wind‐forced KWs insufficiently displace the thermocline (e.g., the boreal winter of 2019 and 2021). The participation of off‐equatorial wave energy hence leads to the diversity of the Atlantic Niños.

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)0.002
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 score0.019
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
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 head0.109
GPT teacher head0.364
Teacher spread0.255 · 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

Citations8
Published2023
Admission routes1
Has abstractyes

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