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Record W1965716286 · doi:10.1175/mwr-d-12-00221.1

Subseasonal Prediction of Wintertime North American Surface Air Temperature during Strong MJO Events

2013· article· en· W1965716286 on OpenAlex
Marcel Rodney, Hai Lin, Jacques Derome

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueMonthly Weather Review · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate variability and models
Canadian institutionsEnvironment and Climate Change CanadaMcGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMadden–Julian oscillationClimatologyDiabaticAnomaly (physics)Persistence (discontinuity)Environmental scienceSurface air temperatureMultivariate statisticsAtmospheric sciencesGeologyMeteorologyClimate changeGeographyOceanographyMathematicsConvectionPhysicsStatistics

Abstract

fetched live from OpenAlex

Abstract A multivariable linear regression model is constructed based on the status of the Madden–Julian oscillation (MJO) and persistence in order to forecast wintertime surface air temperature anomalies over North America out to 4 pentads (20 days). The current and previous states of the MJO are utilized as predictors, based on the Real-time Multivariate (RMM) indices of Wheeler and Hendon. Beyond the persistence-driven first pentad, potentially useful skill is mainly observed during strong MJO events in phases 3, 4, 7, and 8, which correspond to a dipole diabatic heating anomaly in the tropical Indian Ocean and western Pacific. This skill is largely centered over the eastern United States and the Great Lakes region during pentads 2 and 3.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.011
GPT teacher head0.217
Teacher spread0.206 · 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