The use of multiplex PCR reactions to characterize populations of lactic acid bacteria associated with meat spoilage
Bibliographic record
Abstract
A rapid, systematic and reliable approach for identifying lactic acid bacteria associated with meat was developed, allowing for detection of Carnobacterium spp., Lactobacillus curvatus, Lact. sakei and Leuconostoc spp. Polymerase chain reaction primers specific for Carnobacterium and Leuconostoc were created from 16S rRNA oligonucleotide probes and used in combination with species-specific primers for the 16S/23S rRNA spacer region of Lact. curvatus and Lact. sakei in multiplex PCR reactions. The method was used successfully to characterize lactic acid bacteria isolated from a vacuum-packaged pork loin stored at 2 degrees C. Seventy isolates were selected for identification and 52 were determined to be Lact. sakei, while the remaining 18 isolates were identified as Leuconostoc spp.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 0.001 |
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".