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Record W2779875812 · doi:10.2151/jmsj.2018-011

Role and Impact of the Urban Environment in a Numerical Forecast of an Intense Summertime Precipitation Event over Tokyo

2017· article· en· W2779875812 on OpenAlexaff
Stéphane Bélair, Sylvie Leroyer, Naoko Seino, Ľuboš Spaček, Vanh Souvanlasy, Danahé Paquin‐Ricard

Bibliographic record

VenueJournal of the Meteorological Society of Japan Ser II · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicUrban Heat Island Mitigation
Canadian institutionsEnvironment and Climate Change Canada
Fundersnot available
KeywordsPrecipitationEnvironmental scienceContext (archaeology)Metropolitan areaFlooding (psychology)ClimatologyMeteorologyPopulationAtmospheric sciencesGeographyGeologyDemography

Abstract

fetched live from OpenAlex

Heavy precipitation fell over Tokyo in the afternoon of 26 August 2011, leading to flooding and major disruptions for the population, businesses, and authorities. Over 150 mm of precipitation was observed over the city center on that day, with hourly accumulations reaching values as high as 90 mm in late afternoon. Numerical forecasts of this case were performed with a 250-m grid spacing version of the Global Environmental Multi-scale (GEM) model in the context of the Tokyo Metropolitan Area Convection Study (TOMACS). Although initialized only from a global 25-km upper-air analysis, results indicate that GEM can produce the intense precipitation over Tokyo at about the right location and time.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.120
Threshold uncertainty score0.215

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.014
GPT teacher head0.251
Teacher spread0.237 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations37
Published2017
Admission routes1
Has abstractyes

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