{"id":"W3202929289","doi":"10.1101/2021.09.23.461528","title":"Rescuing Biologically Relevant Consensus Regions Across Replicated Samples","year":2021,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Genomics and Chromatin Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"","keywords":"Scripting language; Computer science; Computational biology; Bioconductor; Exploit; Identification (biology); R package; Biology; Data mining; Genetics; Gene; Programming language","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":[0.0103856,0.001630269,0.001807281,0.001888036,0.001860957,0.001749643,0.002183369,0.001918385,0.006143933],"category_scores_gemma":[0.03699773,0.001055998,0.002466338,0.001738644,0.001561754,0.0007142519,0.002159479,0.002368167,0.004736567],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008795664,"about_ca_system_score_gemma":0.001917002,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00327264,"about_ca_topic_score_gemma":0.005758281,"domain_scores_codex":[0.9919187,0.00166465,0.0006631468,0.003799558,0.001518679,0.0004353102],"domain_scores_gemma":[0.9785088,0.009026702,0.00116523,0.00773898,0.003051947,0.0005084181],"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":[0.002043016,0.0002742955,0.04847728,0.003717146,0.001593007,0.001843771,0.001910608,0.0230484,0.7685641,0.006523471,0.01829478,0.1237102],"study_design_scores_gemma":[0.0003204187,0.0004340887,0.07321676,0.0003104168,0.001256509,0.002224696,0.0007028279,0.1657716,0.6799396,0.02351574,0.05200923,0.0002981525],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2410051,0.001661324,0.7040237,0.0004577721,0.0009829018,0.0004749298,0.02111762,0.02725568,0.003021019],"genre_scores_gemma":[0.3738629,0.0002341072,0.5852595,0.0005932099,0.0001217699,0.001097653,0.0263137,0.01003697,0.002480183],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0103856,"threshold_uncertainty_score":0.05492496,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01890904295473806,"score_gpt":0.240621409918142,"score_spread":0.2217123669634039,"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."}}