{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001837895,0.001194945,0.001720271,0.001444036,0.0008173321,0.0007352911,0.001882411,0.001095285,0.00181945],"category_scores_gemma":[0.005503446,0.0004781254,0.001122423,0.001341386,0.0005157123,0.001458466,0.001450476,0.001413716,0.001210175],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006872244,"about_ca_system_score_gemma":0.001918358,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004206968,"about_ca_topic_score_gemma":0.004367418,"domain_scores_codex":[0.9979372,0.0004896098,0.00009595492,0.000434725,0.0008169854,0.0002253906],"domain_scores_gemma":[0.9962819,0.001700267,0.0004274781,0.0004567772,0.0009172136,0.0002163729],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006195455,0.0006149393,0.02075082,0.0002151907,0.00021187,0.0003893617,0.0001816773,0.5750462,0.0186043,0.003967536,0.008677945,0.3707205],"study_design_scores_gemma":[0.0000086166,0.00001752805,0.0004559818,0.000002246428,0.000009137889,0.0000248738,0.000009235468,0.9968118,0.001600143,0.0009035664,0.000152247,0.000004571279],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1794458,0.0004251625,0.8135783,0.0002711019,0.00004760009,0.0001015204,0.0002345116,0.004134229,0.001761756],"genre_scores_gemma":[0.788595,0.0001494258,0.2074551,0.0001606187,0.00008071857,0.00015407,0.001200155,0.0002797414,0.001925067],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004206968,"threshold_uncertainty_score":0.009719849,"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."}}