{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001585194,0.001780929,0.002268656,0.005852313,0.0009201562,0.005066008,0.004120457,0.001696507,0.02814296],"category_scores_gemma":[0.005530332,0.001609852,0.001139968,0.008920093,0.0006267963,0.005505471,0.003489523,0.001839287,0.02118574],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001247922,"about_ca_system_score_gemma":0.002719355,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003463703,"about_ca_topic_score_gemma":0.002861084,"domain_scores_codex":[0.9989857,0.00014222,0.0001497203,0.0003024605,0.0003083528,0.0001115576],"domain_scores_gemma":[0.9980375,0.0003381052,0.0003447752,0.0007368887,0.0002398633,0.0003028431],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.003702058,0.0002941612,0.01106084,0.007524273,0.0005282748,0.001480254,0.00126419,0.008311266,0.05343736,0.07600471,0.6234851,0.2129075],"study_design_scores_gemma":[0.0004876656,0.0001576929,0.007035969,0.0005621345,0.0001820317,0.000782455,0.0004022689,0.0112918,0.02099245,0.03838907,0.9195238,0.0001925898],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"software","genre_scores_codex":[0.01210169,0.003603148,0.09559548,0.0007611087,0.0002217259,0.0003770576,0.7596766,0.1082128,0.01945037],"genre_scores_gemma":[0.02244858,0.002349291,0.06471636,0.0002962448,0.00007203811,0.0004107353,0.8991362,0.006705588,0.00386488],"genre_candidate":"software","genre_consensus":null,"teacher_disagreement_score":0.02814296,"threshold_uncertainty_score":0.09414756,"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."}}