Additional file 1 of Belowground microbiota analysis indicates that Fusarium spp. exacerbate grapevine trunk disease
Bibliographic record
Abstract
Additional file 1: Table S1. The environmental temperature conditions during the seasons of 2019 and 2020. Table S2. The physical and chemical characteristics of the soil in the sampling vineyard. Table S3. The records of chemical agents and fertilizers in the sampling vineyard. Table S4. The reads numbers used to analyze in this study. Table S5. Alpha diversity index values for fungal communities in different soil–plant compartments. PE: rhizoPlane and Endophyte; R: rhizosphere; and B: bulk soils. Samples were collected from grapevine trunk disease (GTD) asymptomatic (As) and symptomatic (S) grapevines in 2019 (0.19) and 2020 (0.20). Table S6. Operational taxonomic units (OTUs) specific to asymptomatic or symptomatic samples. Table S7. The relative abundances of grapevine trunk disease (GTD)-associated fungi annotated at the species and genus levels detected with ITS amplicon sequencing data. Table S8. Grapevine trunk disease (GTD)-associated fungi exhibiting significant differences in relative abundance between comparison groups based on ITS amplicon sequencing. Statistical differences were evaluated with Wilcoxon tests. Table S9. Disease index values for grapevines inoculated with isolates in this study.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.398 | 0.081 |
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".