Additional file 2 of Accelerated discovery of novel glycoside hydrolases using targeted functional profiling and selective pressure on the rumen microbiome
Bibliographic record
Abstract
Additional file 1: Fig. S1. Feed conversion ratio (FCR) and average daily gain (ADG) in bulls fed forage diets and divided into two groups of feed efficiency: 1) efficient or low feed conversion rate (L-FCR) and 2) inefficient or high-FCR (H-FCR). Fig. S2. Top 10 most abundant phyla detected in the rumen metatranscriptome of cattle fed forage-based diets. Fig. S3. Top 10 most abundant Level 2 functions detected in the rumen metatranscriptome of cattle fed forage-based diets. Fig. S4. Analysis of multivariate homogeneity of group dispersions between H-FCR and L-FCR. Fig. S5. Protozoa and fungi species identified in the rumen metatranscriptome according to the feed efficiency groups. Fig. S6. (A) Homology model of xylanases identified in the rumen microbiome aligns well with crystal structures of xylanases of the GH11 family. Fig. S7. (A) Homology model of xylanases and endoglucanases identified in the rumen microbiome bound to the predicted ligands. Table S1. Ingredient and chemical composition of the forage diet used in this study. Table S2. Details of the homology models predicted by I-TASSER. Amino acid sequences of the ten most abundant GH11 and GH45 enzymes identified in the rumen metatranscriptome. Table S3. Details of the homology models predicted by I-TASSER [3, 4].
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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 | 0.003 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.840 | 0.142 |
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".