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Record W4292865352 · doi:10.1038/s41561-022-00999-y

Mid-Pliocene El Niño/Southern Oscillation suppressed by Pacific intertropical convergence zone shift

2022· article· en· W4292865352 on OpenAlexaff
Gabriel M. Pontes, Andréa S. Taschetto, Alex Sen Gupta, Agus Santoso, Ilana Wainer, Alan M. Haywood, Wing‐Le Chan, Ayako Abe‐Ouchi, Christian Stepanek, Gerrit Lohmann, Stephen J. Hunter, Julia C. Tindall, Mark A. Chandler, Linda E. Sohl, W. R. Peltier, Deepak Chandan, Youichi Kamae, Kerim H. Nisancioglu, Zhongshi Zhang, Camille Contoux, Ning Tan, Qiong Zhang, Bette L. Otto‐Bliesner, Esther C. Brady, Ran Feng, Anna S. von der Heydt, Michiel Baatsen, Arthur Oldeman

Bibliographic record

VenueNature Geoscience · 2022
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsUniversity of Toronto
FundersNational Cancer InstituteJapan Agency for Marine-Earth Science and TechnologyJoint Research CentreFP7 Ideas: European Research CouncilGrand Équipement National De Calcul IntensifNederlandse Organisatie voor Wetenschappelijk OnderzoekJapan Society for the Promotion of ScienceCentre for Southern Hemisphere Oceans ResearchCommonwealth Scientific and Industrial Research OrganisationVetenskapsrådetAustralian GovernmentFundação de Amparo à Pesquisa do Estado de São PauloUniversity of LeedsClimate ExtremesNational Science Foundation
KeywordsIntertropical Convergence ZoneClimatologyConvergence zoneGeologyClimate modelSouthern HemisphereWalker circulationEl Niño Southern OscillationMultivariate ENSO indexClimate changeLa NiñaOceanographyPrecipitationGeographyMeteorology

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.050

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.007
GPT teacher head0.233
Teacher spread0.226 · 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

Citations26
Published2022
Admission routes1
Has abstractno

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