Additional file 1 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 1: Supplementary File 1, This Excel file contains eight sheets related to the transcriptomic analysis of gene expression in resistant and susceptible corn lines under different treatments with Clavibacter nebraskensis. Sheet 1 – Supplementary Table S1: Summary of the number of upregulated and downregulated genes identified in each of the six focal treatment comparisons using DESeq2. DEGs were defined based on an adjusted p-value (FDR) < 0.1 and an absolute log2 fold change >1.5. Sheet 2 – FPKM_allsamples: Normalized gene expression data (FPKM values) for all genes across the 18 RNA-seq samples, used as input for downstream analyses such as clustering and visualization. Sheets 3 –8– Full DEG Lists for Six Pairwise Comparisons. Each sheet provides the complete list of differentially expressed genes for one of the six treatment comparisons, including gene IDs, log2 fold changes, and adjusted p-values. The six comparisons are as follows: Sheet 3: 447BACT vs 447CTL, Sheet 4: 450BACT vs 450CTL, Sheet 5: 447DOAB vs 447CTL, Sheet 6: 450DOAB vs 450CTL, Sheet 7: 450DOAB vs 447DOAB and sheet 8: 450BACT vs 447BACT.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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 teacher head, 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".