Bibliographic record
Abstract
Foodborne illness outbreaks associated with the consumption of leafy green vegetables are a growing public health concern worldwide.While there has been great progress in using practical and cost-effective interventions to reduce the risk of contaminations and prevalence of pathogens on farm, effective post-harvest interventions to remove field acquired contamination are still lacking.The body of literature related to microbial hazards in leafy green vegetables has accumulated since 1990, offering often contradictory information on the efficacy of food safety interventions.In this work, I identified, characterized, and assessed the quality of available research on prevalence, risk factors, and interventions for 16 microbial hazards in leafy green vegetables.Systematic literature review, a replicable two-level relevance screening, and a two-phase quality assessment and data extraction procedure were performed by two independent reviewers following general principles of systematic review methodology.A lack of well designed, executed, and reported prevalence studies investigating the efficacy of intervention(s) under real-life conditions was observed.Additional identified knowledge gaps and research areas included equipment sanitation and cross-contamination potential, survival of pathogens in organic leafy greens, and the lack post-harvest intervention studies applicable to the developing regions.
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 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 | 0.015 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".