A Brief History of Endophyte Detection Techniques in Grasses
Bibliographic record
Abstract
Endophytes are the plant mutualists that live asymptomatically inside plant tissue and are found in nearly whole plant kingdom. Endophytic fungi receive shelter and nutrition from host plants and in return provide great advantages to the host. Grasses are a useful forage species and are of great agricultural and socio-economic value. The presence of endophytes in these grasses provide protection, persistence and improved yield against herbivores, insects, pathogens, drought and several other biotic and abiotic stresses. This review summarizes traditional and modern molecular techniques to identify endophytes from turf and forage grasses. Traditional approaches include direct observation, staining, laser micro dissection and pressure catapulting and cultivation-dependent methods that provide a morphological identification of endophytic mycobiota in grass tissues. Earlier studies on endophytes using these methods resulted in several technical implications which molecular approaches are able to solve now-a-days. Molecular approaches include DNA extraction, PCR based DNA Fingerprinting techniques, Denaturing Gradient Gel Electrophoresis, Sanger sequencing, Pyrosequencing, Immunoblot assay, Biosensors, DNA Barcoding and Molecular Phylogenetics etc. A comparison of these detection techniques will facilitate other researchers as well to develop new ways for the detection of endophytes that will contribute to the improvement of grassland in future.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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".