Additional file 2 of Single-cell transcriptome analysis reveals liver injury induced by glyphosate in mice
Bibliographic record
Abstract
Additional file 2: Table S1. The top 100 genes for 13 major cell lineages. Table S2. The cell number for 13 major cell lineages. Table S3. The differentially expressed genes for 13 major cell lineages after glyphosate treatment. Table S4. The cell number for HSC cell subtypes. Table S5. The differentially expressed genes for HSC cell after glyphosate treatment. Table S6. The GO analysis for upregulated DEGs of HSC cells after GLY treatment. Table S7. The cell number for hepatocyte cell subtypes. Table S8. The differentially expressed genes for hepatocyte cell subtypes. Table S9. The differentially expressed genes for hepatocyte cell subtypes after GLY treatment. Table S10. The GO analysis for upregulated DEGs of hepatocyte cells after GLY treatment. Table S11. The cell number for Kupffer cell subtypes. Table S12. The average expression level of differentially expressed genes of KC4 cell for each sample. Table S13. The average fold change (avg_logFC) for differentially expressed cytokines of each Kupffer cell subtype after GLY treatment. Table S14. The average expression level of common differentially expressed genes of KC cell subtypes for each sample. Table S15. The cell number for LCM cell subtypes. Table S16. The differentially expressed genes for LCM cell subtypes after glyphosate treatment.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.326 | 0.086 |
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".