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Record W3003984117 · doi:10.3390/jrfm13020022

Risk Management Analysis for Novel Coronavirus in Wuhan, China

2020· article· en· W3003984117 on OpenAlexaffvenue
Xiao‐Guang Yue, Xuefeng Shao, Rita Yi Man Li, M. James C. Crabbe, Lili Mi, Siyan Hu, Julien S. Baker, Gang Liang

Bibliographic record

VenueJournal of risk and financial management · 2020
Typearticle
Languageen
FieldMathematics
TopicCOVID-19 epidemiological studies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsChinaCoronavirus disease 2019 (COVID-19)OutbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)CoronavirusRisk managementIdentification (biology)2019-20 coronavirus outbreakBusinessEnvironmental planningPandemicWarning systemPneumoniaRisk assessmentGeographyPolitical scienceEnvironmental healthRisk analysis (engineering)Environmental resource managementMedicineVirologyComputer securityComputer scienceEconomicsFinanceDiseaseInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

Recently, a novel coronavirus pneumonia (2019–nCoV) outbreak occurred in Wuhan, China, rapidly spreading first to the whole country, and then globally, causing widespread concern. From the perspectives of early warning and identification of risk, risk monitoring, and analysis, as well as risk management and handling, we propose corresponding solutions and recommendations, which include institutional cooperation, and to inform national and international policy-makers.

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)0.001
Version: codex-gemma-dda1882f352aValidation 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: none
Teacher disagreement score0.751
Threshold uncertainty score0.597

Codex and Gemma teacher scores by category

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

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 head0.127
GPT teacher head0.367
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 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

Citations68
Published2020
Admission routes2
Has abstractyes

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