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Record W2057121978 · doi:10.1371/journal.pone.0095430

Rural Villagers and Urban Residents Exposure to Poultry in China

2014· article· en· W2057121978 on OpenAlexaboutno aff
Zhibin Peng, Peng Wu, Li Ge, Richard Fielding, Xiao‐Wen Cheng, Weike Su, Min Ye, Ying Shi, Qiaohong Liao, Hang Zhou, Lei Zhou, Leilei Li, Jiabing Wu, Shunxiang Zhang, Zhangda Yu, Xiaomin Wu, Hanwu Ma, Jianhua Lu, Benjamin J. Cowling, Hongjie Yu

Bibliographic record

VenuePLoS ONE · 2014
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Disease Management and Epidemiology
Canadian institutionsnot available
FundersMinistry of Science and Technology of the People's Republic of China
KeywordsChinaInfluenza A virus subtype H5N1Rural areaPoultry farmingQuarter (Canadian coin)SocioeconomicsGeographyEnvironmental healthPopulationVeterinary medicineMedicine

Abstract

fetched live from OpenAlex

Patterns of poultry exposure in rural and urban areas in China have not been systematically evaluated and compared. The objective of our study is to investigate patterns in human exposure to poultry in rural and urban China. We conducted a two-stage household-based clustered survey on population exposure to live/sick/dead poultry in Xiuning and Shenzhen. Half of the rural households (51%) in Xiuning raised poultry, mostly (78%) free-range. Around half of those households (40%) allowed poultry to stay in their living areas. One quarter of villagers reported having contact with sick or dead poultry. In Shenzhen, 37% urban residents visited live poultry markets. Among these, 40% purchased live poultry and 16% touched the poultry or cages during purchase. Our findings indicated that human exposure to poultry was different in rural and urban areas in China. This discrepancy could contribute to the observed differences in epidemiologic characteristics between urban and rural cases of influenza A(H7N9) and A(H5N1) virus infection.

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)0.001
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 score0.027
Threshold uncertainty score0.054

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.025
GPT teacher head0.207
Teacher spread0.182 · 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

Citations17
Published2014
Admission routes1
Has abstractyes

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Same venuePLoS ONESame topicAnimal Disease Management and EpidemiologyFrench-language works237,207