How Novel Methods Can Help Discover More Information about Foodborne Pathogens
Bibliographic record
Abstract
Considerable emphasis is being placed on quantitative risk assessment modelling as a basis for regulation of trade in food products. However, for models to be accurate, information about the behaviour of potential pathogens in foods needs to be available. The question is how to obtain this knowledge in a simple and cost effective way. One technique that has great potential is the use of reporter bacteria which have been genetically modified to express a phenotype that can be easily monitored, such as light production in luminescent organisms. Bacteria carrying these (lux) genes can easily be detected using simple luminometers or more sophisticated low light imaging equipment.By monitoring light output from these bacteria over time, it can easily be determined if the organism is growing (resulting in an increase in light emission), is dead (causing a decrease in light production) or is injured (light output remains constant). The use of imaging systems allows the response of bioluminescent bacteria to be studied directly on the food, making the technique even more useful. Applications of bioluminescence are discussed below and include use as reporters of gene expression; biocide efficacy and antibiotic susceptibility; sub-lethal injury; adhesion and biofilm formation; the microbial ecology of foods; pathogenesis; and as biosensors.
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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.014 | 0.009 |
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".