{"id":"W3022317504","doi":"","title":"Bioinformatics and Visual Genomics: Seeing Genes, Proteins and Metabolism","year":2004,"lang":"en","type":"article","venue":"Drug Metabolism Reviews","topic":"Genetics, Bioinformatics, and Biomedical Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Genomics; Computational biology; Gene; Biology; Structural genomics; Genetics; Drug metabolism; Bioinformatics; Genome; Metabolism; Biochemistry; Protein structure","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":[0.00121049,0.0003570428,0.0006049535,0.0001348943,0.0001937947,0.0001343735,0.0002647052,0.000191892,0.00001247861],"category_scores_gemma":[0.0003037424,0.0002751723,0.000142476,0.0001708587,0.0003319942,0.00002514912,0.000445359,0.0002126822,0.0000582915],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001569058,"about_ca_system_score_gemma":0.0001584406,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004614583,"about_ca_topic_score_gemma":0.00003282401,"domain_scores_codex":[0.9978459,0.0001081078,0.0008049987,0.0003791359,0.0003167379,0.0005451405],"domain_scores_gemma":[0.9988543,0.00001296952,0.0002342859,0.0003781809,0.0001062649,0.000413985],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00004826317,0.0001332596,0.0001310596,0.00102987,0.0001491386,0.000004875682,0.001359559,0.000008226812,0.1626796,0.0006203052,0.001418624,0.8324172],"study_design_scores_gemma":[0.001117654,0.00005797664,0.001070411,0.00006145346,0.00008752015,0.00004163794,0.0002258315,0.0002174927,0.04098452,0.0002518298,0.95548,0.0004037221],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"review","genre_scores_codex":[0.5978209,0.3880512,0.009637838,0.0007226919,0.0004565133,0.002169611,0.00006453552,0.0000364728,0.001040272],"genre_scores_gemma":[0.1204684,0.7593458,0.1138348,0.002801745,0.001688566,0.0002584738,0.0003358354,0.0001082428,0.001158126],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.9540613,"threshold_uncertainty_score":0.99997,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01621893848017561,"score_gpt":0.2826219525777087,"score_spread":0.2664030140975331,"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."}}