Rapid detection of fungal endophytes in grasses for large‐scale studies
Bibliographic record
Abstract
Summary Standard visual screening methods for determining the qualitative and quantitative presence of fungal endophytes are too time‐consuming for large‐scale ecological studies. We investigated whether commercially available immunoblot kits, using monoclonal antibody techniques and designed for rapid‐screening of the presence of Neotyphodium endophytes in fresh samples of the pasture grasses Festuca arundinacea and Lolium perenne , could be used for Neotyphodium detection using other grasses and preserved samples. We also determined whether immunoblot kits could provide quantitative information about the amount of Neotyphodium in the grass. The kits accurately detected endophyte presence in F. rubra , F. ovina , F. pratensis and F. altaica , in both preserved samples (dried and fixed), including 12‐year‐old stored, dried samples of F. rubra . Endophytes were detected in 7‐day‐old seedlings of Lolium perenne , 3 days (30%) earlier than previously recognized. The intensity of the coloured tissue prints on scanned immunoblot cards was significantly positively correlated with hyphal density, demonstrating a previously unrecognized accurate quantitative application. These findings greatly reduce logistical barriers to large‐scale field research into the broader ecological significance of Neotyphodium in temperate and arctic grasses in non‐agricultural ecosystems (particularly in remote areas) and suggest potential for estimating historical infection rates using stored and herbarium specimens.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".