Additional file 2 of Grapevine bacterial communities display compartment-specific dynamics over space and time within the Central Valley of California
Bibliographic record
Abstract
Additional file 2. Table S1 Characteristics of the vineyard blocks used within the study. Table S2 Soil elemental composition of the vineyard blocks used within the study. Values are reported in mg/kg (ppm) with the exception of pH which is reported in the standard scale. Table S3 Optimal hyperparameters for training machine learning models. Table S4 Linear model results for soil texture. Type-2 ANOVA table. Table S5 Linear model results for soil elemental composition principal components (PC). Type-2 ANOVA table. Table S6 Linear model results for Bray-Curtis Dissimilarity for soil samples. Type-2 ANOVA table. Table S7 Linear model results for alpha diversity statistics for soil samples, Chao1 index and Faith’s phylogenetic diversity. Type-2 ANOVA table. Table S8 Linear model results for sugar content of the berries. Type-3 ANOVA table. Table S9 Linear mixed model results for Faith’s Diversity index. Type-3 ANOVA table. Table S10 Linear mixed model results for Chao1 index. Type-3 ANOVA table. Table S11 Linear model results for root compartment taxa across taxonomic levels for each experimental factor. Table S12 Linear model results for leaf compartment taxa across taxonomic levels for each experimental factor. Table S13 Linear model results for berry compartment taxa across taxonomic levels for each experimental factor. Table S14 Output statistics for three-class machine learning models predicting rootstock genotype, collection site, and plant compartment. Table S15 Output statistics for binary class machine learning models predicting scion genotype and collection year. In these models Cabernet Sauvignon and 2018 represented the positive class.
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 | 0.011 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.828 | 0.201 |
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".