{"id":"W4250603506","doi":"10.4018/978-1-5225-8903-7.ch003","title":"Bioinformatics","year":2019,"lang":"en","type":"book-chapter","venue":"Biotechnology","topic":"Genetics, Bioinformatics, and Biomedical Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Convergence (economics); Political science; Latin Americans; Data science; Biology; Biotechnology; Computer science; Economic growth; Economics; Law","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","research_integrity","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002200414,0.0004405804,0.0004436476,0.0003298877,0.00006869908,0.00003387927,0.0008522376,0.003191076,0.0002931811],"category_scores_gemma":[0.0001547967,0.0003966345,0.0002384932,0.00004490574,0.0006662456,0.00000283556,0.0008027956,0.0006368841,0.002722467],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004180939,"about_ca_system_score_gemma":0.0003851363,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000305082,"about_ca_topic_score_gemma":0.00001627124,"domain_scores_codex":[0.9980029,0.000008746379,0.0005921628,0.000438244,0.0003998164,0.0005580744],"domain_scores_gemma":[0.9981955,0.00001680931,0.0002660103,0.001223678,0.000139839,0.0001581072],"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":[0.0002176656,0.0001202134,0.00004364416,0.001224442,0.0009976495,0.00004194095,0.0000716897,0.00000714884,0.06068541,0.1341422,0.3587858,0.4436622],"study_design_scores_gemma":[0.000365664,0.0007281291,0.000006854745,0.00005631766,0.00002916553,0.00004425922,0.00002286877,0.0001124636,0.01941804,0.001508372,0.9772304,0.0004774743],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.0004737574,0.00244897,0.003333537,0.002201767,0.001189886,0.0009443338,0.0002973446,0.0001577043,0.9889527],"genre_scores_gemma":[0.002497177,0.01259069,0.005862651,0.0009824279,0.000576506,0.00001124905,0.00122221,0.0001284582,0.9761286],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.6184446,"threshold_uncertainty_score":0.9998485,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01554387657666277,"score_gpt":0.2510502464246959,"score_spread":0.2355063698480331,"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."}}