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Explaining Extreme Events of 2018 from a Climate Perspective

2020· article· en· W3006987757 on OpenAlex

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.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueBulletin of the American Meteorological Society · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicDisaster Management and Resilience
Canadian institutionsnot available
FundersJapan Science and Technology AgencyBureau of ReclamationOffice of ScienceJapan Society for the Promotion of ScienceNanjing UniversityNanjing University of Information Science and TechnologyChina Scholarship CouncilBNP Paribas CardifDepartment for Environment, Food and Rural Affairs, UK GovernmentNational Natural Science Foundation of ChinaNational Oceanic and Atmospheric AdministrationSwedish Foundation for International Cooperation in Research and Higher EducationU.S. Department of EnergyEuropean CommissionUniversity of TorontoMet OfficeAustralian GovernmentMinistry of Education, Culture, Sports, Science and TechnologyNational Centers for Environmental InformationDartmouth CollegeKey Laboratory of Meteorological DisasterNational Science Foundation
KeywordsDownloadPerspective (graphical)Computer scienceResolution (logic)High resolutionFile formatData scienceWorld Wide WebRemote sensingGeographyDatabaseArtificial intelligence

Abstract

fetched live from OpenAlex

Abstract Editors note: For easy download the posted pdf of the Explaining Extreme Events of 2018 is a very low-resolution file. A high-resolution copy of the report is available by clicking here . Please be patient as it may take a few minutes for the high-resolution file to download.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.211
Threshold uncertainty score0.925

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 studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.044
GPT teacher head0.286
Teacher spread0.242 · 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