{"id":"W6958138376","doi":"10.6084/m9.figshare.13070941.v1","title":"Additional file 5 of Development and comparison of RNA-sequencing pipelines for more accurate SNP identification: practical example of functional SNP detection associated with feed efficiency in Nellore beef cattle","year":2020,"lang":"en","type":"article","venue":"Figshare","topic":"Genetic and phenotypic traits in livestock","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta; University of Guelph","funders":"","keywords":"Beef cattle; Pipeline transport; SNP; Pipeline (software)","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002690204,0.00134932,0.001228922,0.002272957,0.001031838,0.001625903,0.002142392,0.001352992,0.7611449],"category_scores_gemma":[0.01679891,0.0008095907,0.00128317,0.002460671,0.0004086814,0.001363625,0.001097315,0.001129455,0.1649827],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008772008,"about_ca_system_score_gemma":0.001409282,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005686543,"about_ca_topic_score_gemma":0.01221069,"domain_scores_codex":[0.9988902,0.0002071073,0.0001347174,0.000346012,0.0002826731,0.0001393582],"domain_scores_gemma":[0.983283,0.01320508,0.0006334276,0.0009643242,0.00154954,0.0003645956],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000556877,0.00009287251,0.003065736,0.002988355,0.0001162365,0.0001359583,0.0001368799,0.00121171,0.001226318,0.0009316475,0.9772728,0.01226459],"study_design_scores_gemma":[0.004322974,0.0003954464,0.04113243,0.002390979,0.0003974472,0.0006782756,0.0003963176,0.006063876,0.006865196,0.0165735,0.9204324,0.0003511211],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"methods","genre_scores_codex":[0.0003300975,0.00003050756,0.001723888,0.00008702864,0.00004079079,0.0000740225,0.9936246,0.002985612,0.001103422],"genre_scores_gemma":[0.008991064,0.0001019988,0.0148919,0.0007074079,0.0001017196,0.001229213,0.9603594,0.005685745,0.007931568],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7611449,"threshold_uncertainty_score":0.3406977,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09564332998207349,"score_gpt":0.2940017747897693,"score_spread":0.1983584448076958,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}