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Record W4286698351 · doi:10.1007/s00376-022-2078-2

Unprecedented Heatwave in Western North America during Late June of 2021: Roles of Atmospheric Circulation and Global Warming

2022· article· en· W4286698351 on OpenAlexaboutno aff
Chunzai Wang, Jiayu Zheng, Wei Lin, Yuqing Wang

Bibliographic record

VenueAdvances in Atmospheric Sciences · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate variability and models
Canadian institutionsnot available
FundersSouthern Marine Science and Engineering Guangdong Laboratory (Guangzhou)Chinese Academy of SciencesNational Natural Science Foundation of China
KeywordsClimatologyAtmospheric circulationGeneral Circulation ModelEnvironmental scienceArcticGreenhouse gasClimate changeClimate modelOceanographyGeographyGeology

Abstract

fetched live from OpenAlex

Abstract An extraordinary and unprecedented heatwave swept across western North America (i.e., the Pacific Northwest) in late June of 2021, resulting in hundreds of deaths, a massive die-off of sea creatures off the coast, and horrific wildfires. Here, we use observational data to find the atmospheric circulation variabilities of the North Pacific and Arctic-Pacific-Canada patterns that co-occurred with the development and mature phases of the heatwave, as well as the North America pattern, which coincided with the decaying and eastward movement of the heatwave. Climate models from the Coupled Model Intercomparison Project (Phase 6) are not designed to simulate a particular heatwave event like this one. Still, models show that greenhouse gases are the main reason for the long-term increase of average daily maximum temperature in western North America in the past and future.

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 score0.916
Threshold uncertainty score0.166

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 communication0.0010.000
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 head0.008
GPT teacher head0.248
Teacher spread0.240 · 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

Citations64
Published2022
Admission routes1
Has abstractyes

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