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Record W3156263552 · doi:10.1038/s43017-021-00155-x

Initialized Earth System prediction from subseasonal to decadal timescales

2021· review· en· W3156263552 on OpenAlexaff
Gerald A. Meehl, Jadwiga H. Richter, Haiyan Teng, Antonietta Capotondi, K. M. Cobb, Francisco J. Doblas‐Reyes, Markus G. Donat, Matthew H. England, John C. Fyfe, Weiqing Han, Hyemi Kim, Ben P. Kirtman, Yochanan Kushnir, Nicole S. Lovenduski, Michael Mann, William J. Merryfield, Verònica Nieves, Kathy Pegion, Nan Rosenbloom, Sara C. Sanchez, Adam A. Scaife, Doug Smith, Aneesh C. Subramanian, Lantao Sun, D. M. Thompson, Caroline C. Ummenhofer, Shang‐Ping Xie

Bibliographic record

VenueNature Reviews Earth & Environment · 2021
Typereview
Languageen
FieldEnvironmental Science
TopicClimate variability and models
Canadian institutionsEnvironment and Climate Change Canada
FundersClimate Program OfficeNational Oceanic and Atmospheric AdministrationEuropean CommissionNational Academies of Sciences, Engineering, and MedicineHorizon 2020 Framework ProgrammeJoint Institute for the Study of the Atmosphere and OceanMet OfficeU.S. Department of EnergyDepartment for Environment, Food and Rural Affairs, UK GovernmentNational Science Foundation
KeywordsPredictabilityClimatologyEarth system scienceForecast skillEnvironmental scienceForecast periodMeteorologyGeologyGeographyOceanographyMathematicsStatistics

Abstract

fetched live from OpenAlex

Initialized climate predictions offer distinct benefits for multiple stakeholders. This Review discusses initialized prediction on subseasonal to seasonal (S2S), seasonal to interannual (S2I) and seasonal to decadal (S2D) timescales, highlighting potential for skilful predictions in the years to come.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.015
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.037
GPT teacher head0.293
Teacher spread0.256 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations239
Published2021
Admission routes1
Has abstractyes

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