{"id":"W2998475082","doi":"10.1101/2020.01.03.893669","title":"Automatic building of protein atomic models from cryo-EM density maps using residue co-evolution","year":2020,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Advanced Electron Microscopy Techniques and Applications","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Nautical Research Society","funders":"","keywords":"Atomic model; Electron density; Algorithm; Resolution (logic); Graph; Chemistry; Computer science; Biological system; Crystallography; Artificial intelligence; Physics; Biology; Electron; Theoretical computer science; Atomic physics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001958106,0.0004336495,0.0004888748,0.00009340106,0.0001436476,0.00005568254,0.0004996678,0.0005882957,0.000006045715],"category_scores_gemma":[0.00006119109,0.0005232284,0.000165502,0.0002153317,0.0001090996,0.00001433552,0.000489025,0.0004576318,0.000003756776],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002372714,"about_ca_system_score_gemma":0.0005754957,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001224899,"about_ca_topic_score_gemma":0.000004395823,"domain_scores_codex":[0.9977921,0.00009561199,0.0005394762,0.0009795275,0.0002044969,0.0003888165],"domain_scores_gemma":[0.9978623,0.00001001854,0.000593261,0.001110243,0.0002743275,0.0001498597],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00003123548,0.00005234823,0.0003379147,0.0001731621,0.00008562618,0.000002730584,0.000003271221,0.0004893977,0.9977769,0.0009528646,0.0000925616,0.000002049288],"study_design_scores_gemma":[0.0002161715,0.0000496025,0.00117608,0.0002809771,0.00008136874,2.183676e-8,0.000001906588,0.008203889,0.9888764,0.0004336654,0.0001891087,0.0004908302],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.670313,0.0007107265,0.3277035,0.00003331748,0.00004327106,0.0007113588,0.0003540365,0.0001296975,0.00000108901],"genre_scores_gemma":[0.8565444,0.00008235902,0.1428707,0.00005046805,0.0002021316,0.0001436749,0.000006621421,0.00009857138,0.000001126969],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1862314,"threshold_uncertainty_score":0.9997219,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01470717871217944,"score_gpt":0.2715725180932752,"score_spread":0.2568653393810958,"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."}}