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Record W2800737098 · doi:10.1029/2017jd028019

Investigating the Effects of Pacific Sea Surface Temperatures on the Wind Drought of 2015 Over the United States

2018· article· en· W2800737098 on OpenAlexaboutno aff
Llorenç Lledó, Omar Bellprat, Francisco J. Doblas‐Reyes, Albert Soret

Bibliographic record

VenueJournal of Geophysical Research Atmospheres · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate variability and models
Canadian institutionsnot available
Fundersnot available
KeywordsClimatologySea surface temperatureWind speedEnvironmental scienceOceanographyPacific decadal oscillationQuarter (Canadian coin)GeographyGeology

Abstract

fetched live from OpenAlex

Abstract During the first quarter of 2015 the United States experienced a widespread and extended episode of low surface wind speeds. This episode had a strong impact on wind power generation. Some wind farms did not generate enough cash for their steady payments, and the value of some assets decreased. Although the wind industry expressed their concerns, the episode has not received much attention from the scientific community and remains weakly understood. In this paper we aim to fill this gap and advance understanding of the underlying processes at seasonal time scales. Using retrospective climate predictions, we find that high sea surface temperatures in the western tropical Pacific ocean associated with a strongly positive phase of the North Pacific Mode played a central role to establish and maintain those wind anomalies. In a more general way it has also been shown that interannual variability of wind speed over North America is not only dominated by El Niño/Southern Oscillation but also by other sea surface temperature variations in the tropical Pacific. This new knowledge can be useful for industry stakeholders to anticipate future periods of low wind speed.

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 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)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.427
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.003
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.028
GPT teacher head0.314
Teacher spread0.285 · 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 teacher head, not a consensus.

Study designBench or experimental
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

Citations57
Published2018
Admission routes1
Has abstractyes

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