{"id":"W4248417034","doi":"10.21203/rs.2.22101/v1","title":"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":"preprint","venue":"Research Square (Research Square)","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 Guelph; University of Alberta","funders":"","keywords":"SNP; Beef cattle; Identification (biology); Computational biology; Biology; Pipeline transport; Pipeline (software); Computer science; Animal science; Single-nucleotide polymorphism; Genetics; Engineering; Gene; Ecology; Genotype; Operating system","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.004553195,0.0003396519,0.0006010071,0.0005074986,0.0003428007,0.00009621906,0.0004972595,0.0005726386,0.00002048806],"category_scores_gemma":[0.004353622,0.0003187724,0.0001131626,0.000808552,0.0009100208,0.00001739235,0.0008380712,0.001421695,0.00000262863],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002624583,"about_ca_system_score_gemma":0.002855787,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004866247,"about_ca_topic_score_gemma":0.0008778507,"domain_scores_codex":[0.994182,0.0009372561,0.001040875,0.001198454,0.001822141,0.0008193017],"domain_scores_gemma":[0.994615,0.001020745,0.0004107249,0.000637245,0.003048732,0.000267536],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.007384307,0.003260748,0.02238711,0.009089358,0.001046425,0.00001344421,0.01770764,0.04847721,0.8548398,0.003193544,0.002706795,0.02989361],"study_design_scores_gemma":[0.003287076,0.003968529,0.3999807,0.001466408,0.00007631137,0.00001108205,0.01319242,0.01098711,0.5621918,0.002536546,0.001392554,0.0009094003],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.93128,0.0008572739,0.06471609,0.0004705489,0.0001213623,0.002308301,0.0001458505,0.00001673026,0.00008389228],"genre_scores_gemma":[0.9902609,0.00006948698,0.00771691,0.000006855603,0.0001989164,0.0006021912,0.0009385892,0.00005618671,0.0001499555],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3775936,"threshold_uncertainty_score":0.9999264,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2364354952468315,"score_gpt":0.432235186727589,"score_spread":0.1957996914807575,"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."}}