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Record W4393161012 · doi:10.3390/applmicrobiol4020041

The Influence of Technological Shifts in the Food Chain on the Emergence of Foodborne Pathogens: An Overview

2024· article· en· W4393161012 on OpenAlexaff
Saja Hamaideh, Amin N. Olaimat, Murad Al‐Holy, Ahmad Ababneh, Hafiz Muhammad Shahbaz, Mahmoud Abughoush, Anas A. Al‐Nabulsi, Tareq M. Osaili, Mutamed Ayyash, Richard A. Holley

Bibliographic record

VenueApplied Microbiology · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFood Safety and Hygiene
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsFood chainFoodborne pathogenFood scienceBiotechnologyBiologyListeria monocytogenesEcologyBacteriaGenetics

Abstract

fetched live from OpenAlex

The transformation of the food chain due to technological advances has had significant implications in regard to food safety. A noteworthy trend in this evolution relates to the emergence of new or previously unseen pathogens within products, thereby altering the landscape of foodborne illness epidemiology. The escalating frequency of these events underscores the need for a comprehensive re-evaluation of preventive strategies. The occurrence of novel species of bacteria, viruses, parasites, and unusual biotoxins from unexpected sources has challenged the previous limits that had been set to prevent foodborne illness outbreaks. The repercussions, ranging from detrimental effects on public health to economic burden, are influenced by a myriad of factors affecting the evolution of foodborne pathogens and emerging ailments. Among these factors are shifts in population demographics and behaviors, especially dietary patterns, as well as climate extremes, advances in more precise pathogen detection, microbial adaptation, evolving agricultural practices, and transformative changes within the food industry. This review critically examines the impact of technological metamorphosis along the food chain, encompassing production, processing, handling, packaging, storage, transportation, and industry demographics on the dynamics influencing the emergence of foodborne pathogens. Additionally, potential solutions to mitigate and manage this escalating issue are proposed.

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)0.001
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.439
Threshold uncertainty score0.191

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.034
GPT teacher head0.238
Teacher spread0.204 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations20
Published2024
Admission routes1
Has abstractyes

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