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Record W2969080398 · doi:10.1038/s41558-019-0542-5

Robustness and uncertainties in global multivariate wind-wave climate projections

2019· article· en· W2969080398 on OpenAlexaff
Joao Morim, Mark Hemer, Xiaolan L. Wang, Nick Cartwright, Claire Trenham, Álvaro Semedo, Ian R. Young, Lucy Bricheno, Paula Camus, Mercè Casas‐Prat, Li Erikson, Lorenzo Mentaschi, Nobuhito Mori, Tomoya Shimura, Ben Timmermans, Ole Johan Aarnes, Øyvind Breivik, Arno Behrens, Mikhail Dobrynin, Melisa Menéndez, Joanna Staneva, Michael Wehner, Judith Wolf, Bahareh Kamranzad, Adrean Webb, Justin E. Stopa, Fernando Pinheiro Andutta

Bibliographic record

VenueNature Climate Change · 2019
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOcean Waves and Remote Sensing
Canadian institutionsEnvironment and Climate Change Canada
FundersU.S. Geological SurveyBiological and Environmental ResearchGriffith UniversityOffice of ScienceUniversidad Nacional Autónoma de MéxicoJapan Society for the Promotion of ScienceNorges ForskningsrådMinistry of Education, Culture, Sports, Science and TechnologyCentre of Excellence for Electromaterials Science, Australian Research CouncilLomonosov Moscow State UniversitySight Research UKNatural Environment Research CouncilNational Energy Research Scientific Computing CenterLawrence Berkeley National LaboratoryAustralian GovernmentU.S. Department of Energy
KeywordsClimate changeClimatologyDownscalingEnvironmental scienceClimate modelMultivariate statisticsRobustness (evolution)MeteorologyWave heightSignificant wave heightWind waveGeographyGeologyStatisticsMathematics

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)0.013
metaresearch head score (Gemma)0.105
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.069

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.105
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0030.005
Open science0.0010.002
Research integrity0.0020.002
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 head0.025
GPT teacher head0.252
Teacher spread0.227 · 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 designSimulation or modeling
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

Citations373
Published2019
Admission routes1
Has abstractno

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