{"id":"W212014443","doi":"10.5220/0002286801270133","title":"FINDING PROTEIN FAMILY SIMILARITIES IN REAL TIME THROUGH MULTIPLE 3D AND 2D REPRESENTATIONS, INDEXING AND EXHAUSTIVE SEARCHING","year":2009,"lang":"en","type":"article","venue":"","topic":"Machine Learning in Bioinformatics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Search engine indexing; Computer science; Information retrieval; Theoretical computer science","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":[],"consensus_categories":[],"category_scores_codex":[0.000223709,0.0001131564,0.0001117573,0.00005226096,0.0001104513,0.00006127535,0.0000648219,0.00009779826,0.000005497695],"category_scores_gemma":[0.000203028,0.0001084452,0.00001534754,0.00006210014,0.00006333952,0.00001986131,0.0001093507,0.0001625945,0.00000154368],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001040873,"about_ca_system_score_gemma":0.00002337785,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001948476,"about_ca_topic_score_gemma":0.00002725085,"domain_scores_codex":[0.9992649,0.00004827852,0.0001933074,0.0002057263,0.00009756551,0.0001902175],"domain_scores_gemma":[0.9997159,0.00003365934,0.00005404614,0.0001352204,0.000024165,0.00003702103],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0001868387,0.00009520347,0.1639913,0.000137692,0.0000503289,0.00001579105,0.006093931,0.001518177,0.8115467,0.00127225,0.0005460731,0.01454569],"study_design_scores_gemma":[0.005700973,0.001339083,0.7411016,0.000427274,0.00003662074,0.00008834255,0.007520362,0.1149572,0.1206875,0.003296571,0.003372471,0.001472007],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9807848,0.0001006734,0.0024241,0.0001598049,0.000007831401,0.0002749907,0.00000468182,0.00002165651,0.01622142],"genre_scores_gemma":[0.9661896,0.0001101865,0.03258918,0.0002019035,0.0000384298,0.000008781371,0.00005061539,0.000008772434,0.000802539],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6908592,"threshold_uncertainty_score":0.4422266,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01629981124323602,"score_gpt":0.3008548505554413,"score_spread":0.2845550393122052,"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."}}