Detection of Listeria monocytogenes by the Nucleic Acid Sequence-Based Amplification Technique
Bibliographic record
Abstract
The rapid detection of foodborne pathogens such as Listeria monocytogenes requires ultrasensitive techniques that give measurable responses with low numbers of the target bacteria in food samples or enrichment cultures. Although a number of approaches are possible for detection of low levels of target bacteria, including enzyme immunoassay (EIA) and nucleic acid probe hybridization, perhaps the most efficient approach from the viewpoint of detectability and specificity are the nucleic acid amplification techniques. Nucleic acid amplification targets specific nucleotide sequences within the bacterial genome and raises the number of copies of the region of interest to levels that are detectable by conventional means (e.g., agarose gel electrophoresis, DNA probes). A well-known example is the polymerase chain reaction (PCR) technique, which amplifies target DNA sequences by a mechanism involving hybridization of two oligonucleotide primers to opposite strands flanking the target region, followed by a repetitive series of cycles involving template denaturation, primer annealing, and the extension of primers by a DNA polymerase, typically the thermostable Taq polymerase, which enables the use of an automated thermal cycling device in this process. The amplification in a typical PCR process can result in a million-fold increase in the number of target sequence copies after 20 cycles ( 1 ). These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.018 |
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".