Identification and application of AFLP-derived genetic markers for quantitative PCR-based tracking of <i>Bacillus</i> and <i>Paenibacillus</i> spp. released in soil
Bibliographic record
Abstract
In this study, we show that noncoding sequences from amplified fragment length polymorphisms (AFLPs) can provide robust and sensitive genetic markers suitable for PCR-based discrimination of closely related strains of Bacillus and Paenibacillus, and quantitative PCR (qPCR)-based tracking of the strains in complex natural systems like soil. Quantitative PCR was accurate in the approximately 1 x 10(9) to approximately 1 x 10(4) colony forming units (CFU)/g soil range. The detection limit was improved to approximately 1 x 10(2) CFU/g when amplicons were analyzed by gel electrophoresis. Studies with laboratory-contained intact soil-core microcosms indicated that environmental persistence trends vary among different strains. For example, Bacillus circulans ATCC 9500, Bacillus amyloliquefaciens DSL 13563-0, Bacillus licheniformis ATCC 12713, Paenibacillus polymyxa NRRL B-4317, and 3 Bacillus subtilisstrains (ATCC 6051A, ATCC 55405, and NRRL B-941) died down to below the 1 x 10(2) CFU/g detection limit by days 28-105. In contrast, over a 105-day period, B. licheniformis ATCC 55406, Bacillus megaterium NRRL B-14308, and P. polymyxa strains ATCC 55407 and DSL 13540-4 died down but persisted at levels just above the detection limit, whereas Bacillus thuringiensis ATCC 13367 experienced a less than 10-fold decrease in cell numbers.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".