Bioassay-Guided Identification of Natural Products for Biocontrol by Thin Layer Chromatography-Direct Bioautography
Bibliographic record
Abstract
Thin layer chromatography-direct bioautography (TLC-DB) is a well-established bioassay used to separate and identify natural products (NPs) that are antagonistic against a target pathogen. It is a rapid, inexpensive, and simple option for the bioassay-guided isolation and identification of NPs that hinges on separation by TLC coupled with the direct application of a target pathogen to examine bioactivity. It is typically used for the analysis of bioactive plant extracts, detecting inhibitory activity against bacteria, fungi, and enzymes. That being said, it has great potential in bacterial NP discovery, particularly for evaluating bacterial NPs against pertinent agricultural pathogens, which is valuable for discovering and developing novel biopesticides for the agriculture industry. Furthermore, it is a tunable protocol that could be applied to other target pathogens or sources of NPs in research programs concerning the discovery and identification of bioactive compounds. Herein, we describe a model system for discovering and identifying biopesticide NPs using TLC-DB with Bacillus spp. and the agricultural pathogen Sclerotinia sclerotiorum.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".