MOESM1 of Genetic architecture of quantitative traits in beef cattle revealed by genome wide association studies of imputed whole genome sequence variants: I: feed efficiency and component traits
Bibliographic record
Abstract
Additional file 1: Figure S1. Distribution of SNP allele substitution effects (left) and additive genetic variances explained by single SNPs (right) for RFI, DMI, ADG, and MWT based on 7.8 M whole genome sequence (WGS) variants; Figure S2. The enriched cellular and molecular functions (top) and cell morphology network (bottom) for RFI; Figure S3. The enriched cellular and molecular functions (top) and carbohydrate metabolism network (bottom) for DMI; Figure S4. The enriched cellular and molecular functions (top) and carbohydrate metabolism network (bottom) for ADG; Figure S5. The enriched cellular and molecular functions (top) and cellular compromise network (bottom) for MWT; Figure S6. Lipid metabolism network for RFI and its component traits DMI, ADG, and MWT; Figure S7. Common lead significant SNPs with the same IDs (a) and candidate genes (b) among RFI and its three component traits at thresholds P-value < 1.00E-05 and FDR < 0.10 based on the imputed 7.8 M DNA variant GWAS; Figure S8. Principal component analyses of Canadian beef cattle populations (N = 7573) based on the 50 K (a) and 7.8 M WGS (b) panels.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.285 | 0.012 |
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".