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Record W2763335573 · doi:10.2807/esw.06.03.02007-en

International outbreak of Salmonella Oranienburg, October-December 2001, Part 3: other countries

2002· article· en· W2763335573 on OpenAlexaff
I. S.T. Fisher, B de Jong, Wilfrid van Pelt, Jeff Aramini, C Berghold, Frieda Matthys, Joan Powling, A. Siitonen

Bibliographic record

VenueWeekly releases (1997–2007) · 2002
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSalmonella and Campylobacter epidemiology
Canadian institutionsHealth Canada
Fundersnot available
KeywordsOutbreakSalmonellaVirologyMicrobiologyGeographyMedicineBiologyBacteria

Abstract

fetched live from OpenAlex

Salmonella enterica subspecies enterica serotype Oranienburg (antigenic formula 6,7:m,t:-) is a comparatively uncommon serotype; data in the Enter-net salmonella database show that in 2000, there were 217 cases of this serotype (out of a total of 122 695 records from 19 countries) and in 1999, 253 cases (out of 135 749 records from 19 countries) reported by the national reference laboratories. Given the size of the outbreak reported from Germany (1), the nature of the infection vehicle, and the likelihood that contaminated chocolate had been exported, an urgent enquiry was sent to all Enter-net participants on 10 December to ascertain whether any associated cases had occurred

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 score0.046
Threshold uncertainty score0.091

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.039
GPT teacher head0.250
Teacher spread0.210 · 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

Citations6
Published2002
Admission routes1
Has abstractyes

Explore more

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