Extraction and detection of baculoviral DNA from lake water, detritus and forest litter
Bibliographic record
Abstract
AIMS: This paper describes a quick, reproducible, sensitive method for baculoviral DNA extraction, purification and detection from freshwater and forest litter environments. METHODS AND RESULTS: The extraction protocol utilizes enzymatic and chemical lysis and physical disruption. To assess the efficiency of the extraction and purification protocol, PCR was used to detect a 530 bp DNA fragment from the genome of a genetically-modified baculovirus, Choristoneura fumiferana NPVegt-/lacZ+. The detection limit of PCR amplification was routinely about 4.1 x 102 occlusion bodies (OBs) 450 microl-1 lake water. Template DNA from the detritus and forest litter samples required 100-fold dilutions before use in PCR reactions. The detection limits for detritus and forest litter samples were routinely about 7.41 x 103 and 2.08 x 104 OBs 0.5 g-1 dry weight, respectively. CONCLUSION: The DNA extraction and purification methodology is reproducible, sensitive and can be used in lieu of, or in conjunction with, insect bioassays. SIGNIFICANCE AND IMPACT OF THE STUDY: The DNA extraction and purification protocol described in this paper will facilitate risk assessment and ecological studies of both wild-type and genetically-modified baculoviruses.
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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) | 0.001 | 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 | 0.000 |
| 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".