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Record W6946370896 · doi:10.33321/cdi.2004.28.46

OzFoodNet: enhancing foodborne disease surveillance across Australia: quarterly report, April to June 2004

2004· article· en· W6946370896 on OpenAlexaboutno aff

Bibliographic record

VenueCommunicable Diseases Intelligence · 2004
Typearticle
Languageen
FieldEnvironmental Science
TopicSpecies Distribution and Climate Change
Canadian institutionsnot available
Fundersnot available
KeywordsOutbreakNorovirusQuarter (Canadian coin)SalmonellaIncidence (geometry)Food poisoningContaminated foodDisease control

Abstract

fetched live from OpenAlex

Salmonella incidence was increased during the quarter, similar to the first quarter of 2004. There were several outbreaks of different phage types of S. Typhimurium occurring in multiple Australian states. OzFoodNet held several discussions during the quarter to try to identify links between these increases. In total, Salmonella infections were responsible for 32 per cent of foodborne outbreaks. Large norovirus outbreaks were reported in association with food service industries where people had worked while ill. It is vital that people responsible for preparing and handling food do not work while they have symptoms of gastroenteritis, as the results can be devastating for food businesses. Imported Japanese oysters were again implicated in an outbreak of suspected viral illness, highlighting the need for improved control measures for these products.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.099
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0170.005

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 head0.043
GPT teacher head0.325
Teacher spread0.282 · 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; both teacher heads agree on what is shown here.

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

Citations0
Published2004
Admission routes1
Has abstractyes

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