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Record W2068280229 · doi:10.1002/jmv.21892

Comparison of the incidence of influenza in relation to climate factors during 2000–2007 in five countries

2010· article· en· W2068280229 on OpenAlexaffabout
Julian W. Tang, Florence YL Lai, Pagbajabyn Nymadawa, Yi‐Mo Deng, Mala Ratnamohan, Martin Petric, Tze Ping Loh, Nancy Wen Sim Tee, Dominic E. Dwyer, Ian Barr, Felix Y.W. Wong

Bibliographic record

VenueJournal of Medical Virology · 2010
Typearticle
Languageen
FieldMedicine
TopicInfluenza Virus Research Studies
Canadian institutionsBC Centre for Disease Control
FundersCity of MelbourneAustralian Government
KeywordsIncidence (geometry)GeographyChinaRelative humidityClimate changeSeasonal influenzaDemographyClimatologySocioeconomicsMeteorologyMedicineCoronavirus disease 2019 (COVID-19)BiologyEcology

Abstract

fetched live from OpenAlex

Relatively few international comparisons of the incidence of influenza related to climate parameters have been performed, particularly in the Eastern hemisphere. In this study, the incidence of influenza and climate data such as temperature, relative humidity, and rainfall, from cities at different latitudes with contrasting climates: Singapore, Hong Kong (China), Ulaanbaatar (Mongolia), Vancouver (Canada), and three Australian cities (Brisbane, Melbourne and Sydney) were examined to determine whether there was any overall relationship between the incidence of influenza and climate. Applying time-series analyses to the more comprehensive datasets, it was found that relative humidity was associated with the incidence of influenza A in Singapore, Hong Kong, Brisbane, and Vancouver. In the case of influenza B, the mean temperature was the key climate variable associated with the incidence of influenza in Hong Kong, Brisbane, Melbourne, and Vancouver. Rainfall was not significantly correlated with the incidence of influenza A or B in any of these cities.

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)0.002
metaresearch head score (Gemma)0.009
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.052
GPT teacher head0.430
Teacher spread0.378 · 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

Citations86
Published2010
Admission routes2
Has abstractyes

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