Bibliographic record
Abstract
Two papers in this issue of the Journal of Infectious Diseases focus on food safety from 2 different angles. First, in an elegant work from Finnish investigators [1], the culprit behind an epidemic caused by Yersinia pseudotuberculosis was uncovered: grated carrots. The same serotype and pulsed-field gel electrophoresis (PFGE) type of Y. pseudotuberculosis was isolated from patients and from the farm where the carrots were grown and processed. The epidemic, which involved an estimated 558 patients, had its root in 1 kitchen preparing food for some 7400 individuals, mostly children in day-care centers or schools. The grated carrots were used in a raw salad without further processing. This first-time isolation of Y. pseudotuberculosis from a contaminated food ingredient resulted from a rapid and relevant reaction from health authorities, as well as sound epidemiological and microbiological work, for which the authors are to be congratulated Although there have been few published reports on this subject, it is not the first time that Y. pseudotuberculosis has caused epidemics: they have been reported from Russia [2], Japan [3, 4], Canada [5], and even previously in Finland [6]. Several food ingredients have been incriminated—for example, carrots [3], lettuce [2], other vegetables [3], and even pickled products [4]. Y. pseudotuberculosis has also been isolated from water and meat products [4]. The bacterium has been found in a range of animals, such as pigs, sheep, deer, hares, rabbits, wild boar, jackal, rodents, birds, and others [7–13]. In these animals, the organism can be cultured from tonsils, tongues, and all larger organs, as well as from the gut [11]. The animal reservoir probably explains how carrot plants can be contaminated on the ground or at any stage of storage if the carrots are not secluded from contact with animals or their feces. Another mode of contamination could be by handling of the products by personnel having contact with animals
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.015 | 0.014 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.025 | 0.025 |
| Insufficient payload (model declined to judge) | 0.039 | 0.013 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".