Additional file 1 of Identification of candidate genes and enriched biological functions for feed efficiency traits by integrating plasma metabolites and imputed whole genome sequence variants in beef cattle
Bibliographic record
Abstract
Additional file 1: Table S1. Additive genetic variances and heritability estimates for 11 metabolites based on the imputed whole genome sequence (WGS) variants in a beef cattle multibreed population; Table S2. Uniquely common candidate genes for feed efficiency traits in a beef cattle multibreed population; Table S3. Enriched biological functions significantly associated with RFI in a beef cattle multibreed population; Table S4. Enriched biological functions significantly associated with DMI in a beef cattle multibreed population; Table S5. Enriched biological functions significantly associated with ADG in a beef cattle multibreed population; Table S6. Enriched biological functions significantly associated with MWT in a beef cattle multibreed population; Table S7. Uniquely common biological functions for feed efficiency traits in a beef cattle multibreed population; Table S8. The comparison of candidate genes between the current study and Zhang et al; Table S9. Genomic breed composition for animals; Table S10. Descriptive statistics of phenotypic data on feed efficiency traits and metabolites.
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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) | 0.002 | 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) | 0.454 | 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".