Isolation and Identification of <i>Campylobacter</i> spp. from Food and Food-Related Environment
Bibliographic record
Abstract
Campylobacter species are among the most common causes of bacterial gastroenteritis in humans worldwide. The genus Campylobacter consists of at least 39 validly published species with wide distribution in various hosts and environments, which are either pathogens for humans or animals, or not pathogenic as identified so far. Various methods have been used for detecting campylobacters including conventional culture methods, molecular (such as polymerase chain reaction), immunological methods and genome sequencing. Currently, isolation and subsequent identification of the target campylobacters are required by most of the regulatory bodies globally. The multiple Campylobacter species exhibit diverse physiological and metabolic characteristics and growth requirements, which can interfere with the sensitivity and specificity of culture-dependent methods. Furthermore, strains among each species may behavior differently in various culture media and under various culture conditions. Therefore, it is important to apply appropriate isolation and identification methods for different types of species and samples based on specific purposes. This chapter will review the development and the current status of culture-dependent methods for the isolation and detection of various Campylobacter species from food and food-related environments during the next generation sequencing era.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.017 |
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".