{"id":"W2953409640","doi":"10.1093/bioinformatics/btz522","title":"Pygenprop: a Python library for programmatic exploration and comparison of organism genome properties","year":2019,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Université de Genève; Nuclear Waste Management Organization","keywords":"Python (programming language); Computer science; Documentation; Genome; Source code; Organism; Software; MIT License; Genomics; Programming language; World Wide Web; Biology; Genetics; Gene","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.00006353777,0.0001052301,0.0001705091,0.00002740715,0.00004329903,0.00002938339,0.00008385198,0.00006783985,0.00000273926],"category_scores_gemma":[0.00001659095,0.00008468707,0.00003740339,0.000040055,0.00004891093,0.000006241983,0.00009839995,0.00002320659,0.000004892061],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000002689896,"about_ca_system_score_gemma":0.00003815564,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":8.145507e-7,"about_ca_topic_score_gemma":0.000001077762,"domain_scores_codex":[0.9994072,0.000008336751,0.0003001411,0.00009482574,0.00006275291,0.0001266941],"domain_scores_gemma":[0.9995987,0.000006408905,0.0001435548,0.0001704448,0.00004966807,0.00003116225],"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":[0.0001353604,0.00009957128,0.008198342,0.001506945,0.0001409448,5.270872e-8,0.004937992,0.0001567666,0.9688927,0.000442658,0.0004828748,0.0150058],"study_design_scores_gemma":[0.002102704,0.003561857,0.004948588,0.00009605425,0.0001064789,0.000008726152,0.008145404,0.02165438,0.8688735,0.0008851323,0.08889751,0.0007196164],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9949567,0.001333245,0.002319895,0.00009532957,0.00007842683,0.0008435688,0.00002941068,0.000008047773,0.0003353538],"genre_scores_gemma":[0.9749147,0.0002189538,0.02439493,0.00004803106,0.00004547964,0.00004056555,0.0000913365,0.00001939179,0.0002265826],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1000191,"threshold_uncertainty_score":0.3453439,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02542589378129637,"score_gpt":0.2317701714092315,"score_spread":0.2063442776279352,"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."}}