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Record W4225417104 · doi:10.1088/1748-9326/ac6c3c

Concurrent hot extremes and high ultraviolet radiation in summer over the Yangtze Plain and their possible impact on surface ozone

2022· article· en· W4225417104 on OpenAlexafffund
Yan Xia, Yongyun Hu, Yi Huang, Jianchun Bian, Chuanfeng Zhao, Jing Wei, Yingying Yan, Fei Xie, Jintai Lin

Bibliographic record

VenueEnvironmental Research Letters · 2022
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric Ozone and Climate
Canadian institutionsMcGill University
FundersCanadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of China
KeywordsEnvironmental scienceAtmospheric sciencesOzoneClimatologyTropospheric ozoneNoonRadiationUltraviolet radiationOzone layerSubtropicsTroposphereMeteorologyGeologyGeographyChemistryEcologyPhysics

Abstract

fetched live from OpenAlex

Abstract Hot extremes, ultraviolet (UV) radiation, and surface ozone all have prominent effects on human health and ecosystems. Here we show evidence that both hot extremes and high surface UV radiation at noon time occur concurrently in summer over the Yangtze Plain. Composite analysis suggests that hot extremes in summer are primarily caused by the westward extension of the Western Pacific Subtropical High, which leads to less clouds and consequently more downward solar radiation on the surface over the Yangtze Plain. It is found that surface UV radiation may be dominated by cloud variations, instead of stratospheric ozone during the hot extremes. Further analysis indicates that the hot extremes and high UV radiation, which play important roles in photochemistry in the troposphere, may result in more surface ozone. The concurrent hot extremes, strong UV radiation, and severe ozone pollutions over the Yangtze Plain in summer are likely to have dramatical influences on human health, which should be paid more attention.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.046
Threshold uncertainty score0.998

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)0.0030.000

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 head0.023
GPT teacher head0.266
Teacher spread0.244 · 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.

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

Citations32
Published2022
Admission routes2
Has abstractyes

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