{"id":"W1920637681","doi":"10.1109/cibcb.2015.7300341","title":"FragGeneScan-plus for scalable high-throughput short-read open reading frame prediction","year":2015,"lang":"en","type":"article","venue":"","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Scalability; Hidden Markov model; ORFS; Frame (networking); Open reading frame; ENCODE; Reading (process); Data mining; Artificial intelligence; Gene; Biology; Database; 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":[0.001447562,0.002870955,0.001431564,0.001329343,0.001278364,0.001705395,0.004199137,0.001140668,0.01231576],"category_scores_gemma":[0.004413695,0.001292346,0.001767832,0.002293134,0.0006398503,0.002194763,0.001334906,0.002042065,0.007877985],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001466607,"about_ca_system_score_gemma":0.002981798,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01453211,"about_ca_topic_score_gemma":0.01509435,"domain_scores_codex":[0.9992125,0.00009684198,0.00004658545,0.000261989,0.0002774669,0.0001046004],"domain_scores_gemma":[0.9988268,0.0005155611,0.0001225502,0.0001769196,0.0002606824,0.0000974874],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.006840948,0.0007082711,0.01420704,0.002328075,0.001180283,0.001353874,0.00092072,0.1717584,0.1373034,0.01924795,0.3330748,0.3110763],"study_design_scores_gemma":[0.0002288527,0.0001291023,0.002356542,0.00005375443,0.0000732711,0.0001703424,0.00009473669,0.9007723,0.05773557,0.008914545,0.02930882,0.000162216],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04541695,0.0005493027,0.5070786,0.0003413524,0.0002171625,0.0003131237,0.02667698,0.4147243,0.004682071],"genre_scores_gemma":[0.2051452,0.0005053402,0.6904396,0.0004449074,0.00008559093,0.001406423,0.0774711,0.01923388,0.005267868],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01453211,"threshold_uncertainty_score":0.04120028,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04852408045817034,"score_gpt":0.2979459020527647,"score_spread":0.2494218215945943,"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."}}