DETECTION OF <i>LISTERIA</i> SP. IN MEAT AND MEAT PRODUCTS USING TECRA <i>LISTERIA</i> VISUAL IMMUNOASSAY AND BIOCONTROL VISUAL IMMUNOPRECIPITATE ASSAY FOR <i>LISTERIA</i> IMMUNOASSAYS AND A CULTURAL PROCEDURE
Bibliographic record
Abstract
ABSTRACT The routine methods for detecting Listeria sp. in foods are time consuming and involve using selective enrichments and plating on agars. In this study, the presence of Listeria sp. in 120 meat and meat product samples was investigated by two rapid immunoassays (TECRA Listeria Visual Immunoassay [VIA] and BioControl Visual Immunoprecipitate Assay [VIP] for Listeria) and a cultural procedure. The cultural method of detecting Listeria sp. followed Canada's Health Protection Branch Method, and the rapid tests followed the manufacturers' instructions. The agreement between the cultural and the rapid tests was established at a confidence limit of 95%. Seventy‐nine samples (65.8%) were Listeria sp. positive in at least one of the three tests. There was no statistically significant difference between the cultural procedure and any of the rapid immunoassays. The agreement rates between the VIA and the cultural method and between the VIP and the cultural method were 87 and 84%, respectively. Both tests – the VIA and VIP – proved to be rapid, efficient and easy to perform.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".