Additional file 2 of Cost-effectively dissecting the genetic architecture of complex wool traits in rabbits by low-coverage sequencing
Bibliographic record
Abstract
Additional file 2: Table S1. Genotype accuracy and concordance of imputation obtained with the three pipelines on 15 individuals. Table S2. Genotype accuracy and concordance of imputation by the three pipelines with the MAF ranges. Table S3. Genotype accuracy and concordance of imputation with different sample size and depth by BaseVar + STITCH. Table S4. Variant analysis by regions and functions. Table S5. LD decay in the Angora rabbit population. Table S6. Genes located in the selected regions in the Angora rabbit population. Table S7. Enrichment of genes located in the selected regions in the Angora rabbit population. Table S8. LD decay in the Angora and wild rabbit populations. Table S9. Genes located in the selected regions between Angora and wild rabbits. Table S10. Enrichment of genes located in the selected regions between Angora and wild rabbits. Table S11. Genes located in the common regions between within-breed and cross-breed signatures of selection. Table S12. Enrichment of genes located in the common regions between within-breed and cross-breed signatures of selection. Table S13. Significant SNPs for DFW. Table S14. Significant SNPs for CVDFW. Table S15. Significant SNPs for LFW. Table S16. Significant SNPs for BW. Table S17. SNP-based heritability. Table S18. QTL contribution to phenotypic variance. Table S19. Genes overlapping with by QTL.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.853 | 0.000 |
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 teacher head, 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".