Additional file 2 of Unveiling molecular mechanisms and candidate genes for goss’s bacterial wilt and leaf blight resistance in corn through RNA-Seq analysis
Bibliographic record
Abstract
Supplementary Material 2: Supp Figure S1A, Screen plot depicting the percent variation explained over the first 18 principal components across all 18 RNA-seq samples. S1B, Elbow plot showing the decay within the sum of squares as a function of the number of clusters for an unsupervised clustered heatmap. Figure S1C, hierarchical heatmap of the top 2000 genes exhibiting the highest standard deviation in expression across all samples. Supp Figure S2, Correlation plot showing the 1:1 correlation in gene expression between the mean of all three samples/treatment for all genes between treatments. Supp Figure S3, Volcano plot of the DEGs between control resistant maize (450) and same maize cultivar inoculated with aggressive (BACT) or weak (DOAB) strains of C. nebraskensis (450BACT vs 450CTL and 450DOAB vs 450CTL) after five days. Up and down (red) regulated genes that had a log2fold change >1.5 and an FDR p-value <0.1 are coloured, while those that are coloured blue had an Adjusted p-value<0.1, but a fold change <1.5. Supp Figure S4, Heatmap of top DEGs across all treatments for the corn line 450 (A), corn line 447 (B) or comparing corn lines 447 vs 450 for the weak (DOAB) or aggressive (BACT) bacterial strains (C).
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.785 | 0.155 |
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".