{"id":"W2001218994","doi":"10.1186/1472-6807-9-28","title":"Improving consensus contact prediction via server correlation reduction","year":2009,"lang":"en","type":"article","venue":"BMC Structural Biology","topic":"Protein Structure and Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs; National Natural Science Foundation of China","keywords":"Computer science; Support vector machine; Voting; Server; Correlation; Protein structure prediction; Data mining; Artificial intelligence; Machine learning; Algorithm; Mathematics; Protein structure","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":[0.00008428178,0.0001828001,0.0001521001,0.00004599745,0.0001221227,0.00001505282,0.0001077791,0.0003586868,0.00001833976],"category_scores_gemma":[0.00007328375,0.0001582905,0.0000842469,0.00007177296,0.00006545397,0.000006214031,0.00003410272,0.0001290691,0.000004882443],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003240633,"about_ca_system_score_gemma":0.00005195193,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005475125,"about_ca_topic_score_gemma":0.00003011226,"domain_scores_codex":[0.9989583,0.00008676202,0.0002529701,0.0003914212,0.00006070152,0.0002497898],"domain_scores_gemma":[0.9994197,0.000008740327,0.000155336,0.0002675884,0.00008732919,0.00006132639],"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":[0.0002443118,0.000003981097,0.008331366,0.000006714322,0.00001819493,6.62882e-7,0.00001480896,0.0002210113,0.9664128,0.001401793,0.00008887829,0.02325551],"study_design_scores_gemma":[0.004650797,0.004524464,0.5489355,0.00002693698,0.000165879,0.001721367,0.0001360686,0.02998064,0.3654413,0.03944419,0.003556028,0.001416795],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9823902,0.000330235,0.01569423,0.00006634317,0.0009496166,0.0002694172,0.00003669109,0.0000484227,0.0002148229],"genre_scores_gemma":[0.9954264,0.00001485813,0.002961887,0.0001253911,0.0006118208,0.000005652224,0.0007556926,0.00001032691,0.00008803869],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6009715,"threshold_uncertainty_score":0.6454899,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006507043961281266,"score_gpt":0.2356594660470491,"score_spread":0.2291524220857678,"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."}}