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Record W4400761071 · doi:10.1080/19475705.2024.2379595

Climatic characteristics of heat wave events (1959–2023) in Jiangxi Province, China

2024· article· en· W4400761071 on OpenAlexaff
Wenying Liu, Suqin Sun, Yuhong He, Ying Zhang, Caiting Huang

Bibliographic record

VenueGeomatics Natural Hazards and Risk · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate Change and Health Impacts
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsHeat waveChinaEnvironmental scienceGeographyPhysical geographyClimatologyClimate changeGeologyOceanographyArchaeology

Abstract

fetched live from OpenAlex

This study investigated the temporal and spatial patterns of heat wave (HW) events in Jiangxi, China. Our analysis is based on the observation data, including daily maximum temperature and daily average air relative humidity from 81 meteorological stations from 1959 to 2023. We also examined the impact of HW events on summer electricity consumption by taking the capital city of Jiangxi, Nanchang, as a case study. The results reveal an overall increase in the frequency of HW events over the past 65 years, with a pronounced increase since 1997, particularly in central and southern Jiangxi. The onset of HW events has generally occurred earlier, while their cessation has been delayed across most areas of Jiangxi Province, resulting in extended HW periods. Economic growth in regions like the Jitai Basin, the Ganfu Plain, and the hilly areas of northeastern Jiangxi has been accompanied by more intense and prolonged HW events. In Nanchang City, the summer electricity load increased by approximately 9% on holidays compared to working days and by about 20% on high-temperature days compared to normal conditions. These insights highlight the need for adaptive strategies to mitigate the impacts of increasing HW occurrences.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.653
Threshold uncertainty score0.522

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.012
GPT teacher head0.265
Teacher spread0.253 · 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

Citations3
Published2024
Admission routes1
Has abstractyes

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