{"id":"W4288286635","doi":"","title":"Multi-variate mixed-models for the normalization of RNA-Seq data: Application to onset of puberty in beef cattle","year":2019,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Genetic and phenotypic traits in livestock","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Normalization (sociology); Random variate; Computer science; RNA-Seq; Mathematics; Biology; Transcriptome; Statistics; Genetics","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":[],"consensus_categories":[],"category_scores_codex":[0.02461237,0.002492451,0.002690218,0.001590823,0.001631372,0.002664297,0.005048793,0.002771049,0.005785285],"category_scores_gemma":[0.02842599,0.002078255,0.007445321,0.002936374,0.00155022,0.001901757,0.002462059,0.006312154,0.003276338],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00175262,"about_ca_system_score_gemma":0.00335167,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0176979,"about_ca_topic_score_gemma":0.03470651,"domain_scores_codex":[0.9913641,0.005551346,0.0003421749,0.00188691,0.0005098018,0.0003456243],"domain_scores_gemma":[0.981528,0.01430209,0.0006928509,0.001730951,0.001442998,0.0003029859],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.002989622,0.0004728076,0.008905921,0.001338781,0.004504724,0.0003080526,0.0008980568,0.5505714,0.01862483,0.02226678,0.01321443,0.3759046],"study_design_scores_gemma":[0.0001153805,0.0001281009,0.002137359,0.00003130895,0.0001700792,0.00004918352,0.00006932535,0.9779614,0.003646506,0.01232461,0.003290239,0.0000764486],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0065374,0.0005547153,0.9870193,0.0001770696,0.0001401027,0.0001251692,0.001248071,0.004086189,0.0001120524],"genre_scores_gemma":[0.08040777,0.0004507188,0.9040438,0.0003338479,0.0002011879,0.002105809,0.006331039,0.002669854,0.003455984],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02461237,"threshold_uncertainty_score":0.1301642,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02297166369312848,"score_gpt":0.2602354885533875,"score_spread":0.237263824860259,"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."}}