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Record W2153361527 · doi:10.1002/2015gl066104

Increasing atmospheric water vapor and higher daily precipitation intensity over northern Eurasia

2015· article· en· W2153361527 on OpenAlexaff
Hengchun Ye, Eric J. Fetzer, Sun Wong, Ali Behrangi, Daqing Yang, Bjorn H. Lambrigtson

Bibliographic record

VenueGeophysical Research Letters · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate variability and models
Canadian institutionsEnvironment and Climate Change Canada
FundersCalifornia Institute of TechnologyJet Propulsion LaboratoryNational Aeronautics and Space AdministrationNational Science Foundation
KeywordsPrecipitationEnvironmental scienceWater vaporHumidityAtmospheric sciencesClimatologyAtmosphere (unit)Intensity (physics)Relative humidityPercentileMeteorologyGeologyGeography

Abstract

fetched live from OpenAlex

Abstract Increasing daily precipitation intensity is strongly associated with increasing water vapor in the atmosphere over northern Eurasia based on this study of 35 years of daily precipitation, specific humidity, and air temperature observations at 152 stations. The apparently linear relationship is consistent across all four seasons at interannual and longer time scales, and holds after temperature variation have been controlled. The study further reveals that this relationship is accompanied by increases in precipitation totals from heavy events (above the 70th percentile) and decreases in light ones (below the 30th percentile). Results suggest that increased atmospheric water vapor is the direct link to more frequent intense events of precipitation and increased risk of flooding under a warming climate via increasing precipitation intensity.

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 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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.019
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.047
GPT teacher head0.296
Teacher spread0.249 · 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 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

Citations49
Published2015
Admission routes1
Has abstractyes

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