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Intervention Strategies to Control Campylobacter at Different Stages of the Food Chain

2023· review· en· W4313473216 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueMicroorganisms · 2023
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicSalmonella and Campylobacter epidemiology
Canadian institutionsMcGill UniversityUniversity of Guelph
Fundersnot available
KeywordsCampylobacterCampylobacter jejuniTransmission (telecommunications)BiologyFoodborne pathogenPoultry farmingFood chainFood safetyBiotechnologyFood scienceBacteriaEcologyEngineeringListeria monocytogenes

Abstract

fetched live from OpenAlex

Campylobacter is one of the most common bacterial pathogens of food safety concern. Campylobacter jejuni infects chickens by 2–3 weeks of age and colonized chickens carry a high C. jejuni load in their gut without developing clinical disease. Contamination of meat products by gut contents is difficult to prevent because of the high numbers of C. jejuni in the gut, and the large percentage of birds infected. Therefore, effective intervention strategies to limit human infections of C. jejuni should prioritize the control of pathogen transmission along the food supply chain. To this end, there have been ongoing efforts to develop innovative ways to control foodborne pathogens in poultry to meet the growing customers’ demand for poultry meat that is free of foodborne pathogens. In this review, we discuss various approaches that are being undertaken to reduce Campylobacter load in live chickens (pre-harvest) and in carcasses (post-harvest). We also provide some insights into optimization of these approaches, which could potentially help improve the pre- and post-harvest practices for better control of Campylobacter.

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.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.968
Threshold uncertainty score0.615

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.051
GPT teacher head0.285
Teacher spread0.234 · 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