The creeping bentgrass microbiome: Traditional culturing and sequencing results compared with metagenomic techniques
Bibliographic record
Abstract
Abstract Recent metagenomic studies have probed the fungal microbiome of intensively managed turfgrasses to better understand the organisms present, which may be beneficial or harmful, but the taxonomic resolution is often limited to the family or genus level. This may relate to the common practice of targeting short ribosomal DNA sequences for estimating fungal abundance and phylogenetic relationships. We collected samples of intensively managed creeping bentgrass (Agrostis stolonifera L.) from Guelph, ON, across two growing seasons and obtained 2,204 foliar epiphytic fungal isolates. Sequencing the entire internal transcribed spacer (ITS) region of 251 representative isolates resolved these to 54 species in 31 genera. A comparison of the taxa identified here versus those reported in five metagenomic studies revealed similarities. However, of the 31 genera we identified by sequencing, 13 genera (42%) were not reported in the metagenomic studies related to intensively managed turfgrass systems. The five metagenomic studies identified an average of 44 genera, with 46% (ranging from 4 to 72%) on average being unique to each study. In addition to revealing genera that were not reported in other studies, full‐length ITS sequencing had the advantage of being able to resolve to the species level. We could resolve 248 of the sequenced isolates to species with an e‐value of 10−50, with three left at the genus level. Until sequencing technologies can yield full‐length ITS sequencing, laborious traditional culturing followed by sequencing of the entire ITS region can give insights into microbiomes not revealed by current metagenomic methods.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".