{"id":"W2146436858","doi":"10.1093/database/bau097","title":"MetaProx: the database of metagenomic proximons","year":2014,"lang":"en","type":"article","venue":"Database","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"University of Waterloo; Natural Sciences and Engineering Research Council of Canada; Wilfrid Laurier University","keywords":"Metagenomics; Computer science; Database; Inference; Operon; Information retrieval; Data mining; Computational biology; Biology; Gene; Genetics; Artificial intelligence","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":[0.0006541167,0.0001257881,0.0001387198,0.00002412065,0.00007868427,0.00001602225,0.0003847806,0.00005157999,0.00003968248],"category_scores_gemma":[0.00008332363,0.00008437246,0.00007956959,0.00006962681,0.0001156682,0.000006242405,0.0003106965,0.00009631803,0.00002986332],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000003365352,"about_ca_system_score_gemma":0.00004001794,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000343838,"about_ca_topic_score_gemma":0.00004828492,"domain_scores_codex":[0.9991887,0.00006528195,0.0002535857,0.0001945085,0.0001019736,0.0001960114],"domain_scores_gemma":[0.9987049,0.00002461182,0.0001284904,0.001037294,0.0000404068,0.00006434294],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001370763,0.0001589172,0.0004208372,0.0001654335,0.0003115762,0.000001789666,0.0001244161,0.0001641341,0.8918225,0.01434624,0.07664429,0.01570281],"study_design_scores_gemma":[0.00099575,0.0002182147,0.0003667967,0.00003185266,0.0001949554,0.00002283694,0.0001427633,0.006841005,0.2573221,0.0003842329,0.7330723,0.0004071561],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7116096,0.005452824,0.2581643,0.001282043,0.0009559113,0.001669927,0.005101923,0.0000495967,0.01571382],"genre_scores_gemma":[0.9878702,0.0002006064,0.007553803,0.0007313217,0.0002823242,0.00003012944,0.002888119,0.00002186805,0.0004216546],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.656428,"threshold_uncertainty_score":0.3440609,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0111480008181741,"score_gpt":0.2336394498063358,"score_spread":0.2224914489881617,"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."}}