Additional file 2 of An integrated gene catalog and over 10,000 metagenome-assembled genomes from the gastrointestinal microbiome of ruminants
Bibliographic record
Abstract
Additional file 1: Fig. S1. Computational pipeline for gene catalog and assembly of MAGs. Fig. S2. DNA contamination statistics. Fig. S3. Assembly statistics. Fig. S4. Comparison of the RGMGC to the public datasets. Fig. S5. Coverage of the RGMGC. Fig. S6. Comparison of the dominant microbial taxa at the genus level among GIT regions. Fig. S7. Functional structure of the GIT microbiome. Fig. S8. Gene diversity in microbial communities across the ruminant GIT. Fig. S9. CheckM quality assessment. Fig. S10. Distribution of 10,373 genomes across the ruminant GIT. Fig. S11. Variations in enrichment of MAGs among GIT regions. Fig. S12. Comparative analysis of genomes of the CAG-110 genus. Fig. S13. Species-level clustering of reference genomes and MAGs. Fig. S14. Distribution of the 8,745 USGs across the ruminant GIT. Fig. S15. Taxonomic composition of the 8,745 USGs. Fig. S16. Biosynthetic gene clusters found in the human gut species. Fig. S17. Differences in GP profiles between the USGs and RCGs. Fig. S18. Comparison of the USGs and RCGs in the prevalent phyla. Fig. S19. Phylogenetic tree of the 194 proteobacteria genomes. Fig. S20. Phylogenetic tree of mutualistic archaea. Fig. S21. Distributions of hydrogenases and associated terminal reductases in the 10,373 MAGs. Fig. S22. Associations of GIT microbial species with cattle feed efficiency (FE).
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.758 | 0.171 |
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".