Development of AFLP-derived, functionally specific markers for environmental persistence studies of fungal strains
Bibliographic record
Abstract
The ability to rapidly identify and quantify a microbial strain in a complex environmental sample has widespread applications in ecology, epidemiology, and industry. In this study, we describe a rapid method to obtain functionally specific genetic markers that can be used in conjunction with standard or real-time polymerase chain reaction (PCR) to determine the abundance of target fungal strains in selected environmental samples. The method involves sequencing of randomly cloned AFLP (amplified fragment length polymorphism) products from the target organism and the design of PCR primers internal to the AFLP fragments. The strain-specific markers were used to determine the fate of three industrially relevant fungi, Aspergillus niger, Aspergillus oryzae, and Chaetomium globosum, during a 4 month soil microcosm experiment. The persistence of each of the three fungal strains inoculated separately into intact soil microcosms was determined by PCR analyses of DNA directly extracted from soil. Presence and absence data based on standard PCR and quantification of the target DNA by real-time PCR showed that all three strains declined after inoculation (approximately 14-, 32-, and 4-fold for A. niger, A. oryzae, and C. globosum, respectively) but remained detectable at the end of the experiment, suggesting that these strains would survive for extended periods if released into nature.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.001 | 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".