Additional file 1 of Seasonal stability of the rumen microbiome contributes to the adaptation patterns to extreme environmental conditions in grazing yak and cattle
Bibliographic record
Abstract
Additional file 1: Table S1. Pasture’s chemical composition at different seasons (on dry matter basis). Table S2. Summary of sequence data generated from rumen samples of grazing yak (n=6) and cattle (n=6). Table S3. Rumen bacterial composition at the phylum level between grazing yak and cattle under different seasons. Table S4. Rumen bacterial composition at species level between grazing yak and cattle at different seasons. Table S5. Rumen archaeal composition at the phylum level between grazing yak and cattle under different seasons. Table S6. Rumen archaeal composition at species level between grazing yak and cattle at different seasons. Table S7. Composition of metabolic pathways based on the first-level and second-level functions in the KEGG. Table S8. Rumen CAZyme profiles between grazing yak and cattle at different seasons. Table S9. The origin of CAZymes in the species of ruminal bacteria. Table S10. ARGs profiles between grazing yak and cattle at different seasons. Table S11. The origin of ARGs in the species of rumen bacteria. Table S12. The information of rumen bacteria that carry both CAZymes and ARGs. Table S13. The rumen metagenomes analyzed in this study.
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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 | 0.011 |
| 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 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.444 | 0.070 |
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".