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Record W2972251634 · doi:10.1029/2019gl084647

Freezing Rain Events Related to Atmospheric Rivers and Associated Mechanisms for Western North America

2019· article· en· W2972251634 on OpenAlexaffabout
Ju Liang, Laxmi Sushama

Bibliographic record

VenueGeophysical Research Letters · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate variability and models
Canadian institutionsMcGill University
Fundersnot available
KeywordsHydrometeorologyClimatologyEnvironmental scienceAdvectionAtmospheric sciencesClimate modelWestern europeClimate changePrecipitationOceanographyGeologyMeteorologyGeography

Abstract

fetched live from OpenAlex

Abstract Atmospheric rivers (ARs) are responsible for hazardous hydrometeorological events over western North America. For the period 2070–2099 relative to 1976–2005, regional climate model simulations driven by two coupled global climate models for the RCP4.5 and 8.5 scenarios project up to 90% increase in AR occurrences over western North America. Results also suggest that the increase in AR‐related freezing rain (FR) events is possibly due to changes in both dynamical and thermal mechanisms associated with AR events. In the west coast of Canada, AR‐related FR events are projected to intensify, which is facilitated by the increased midlevel ascent related to differential vorticity advection associated with ARs. The fraction of AR‐related FR amount is projected to increase over the inland of western U.S. and the west coast of Canada, which is possibly driven by the increased temperature difference between the subfreezing near‐surface layer and the above‐freezing layer aloft.

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 score0.339
Threshold uncertainty score0.675

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.022
GPT teacher head0.283
Teacher spread0.262 · 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

Citations19
Published2019
Admission routes2
Has abstractyes

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