{"id":"W2323614149","doi":"10.1093/bioinformatics/btw129","title":"mDCC_tools: characterizing multi-modal atomic motions in molecular dynamics trajectories","year":2016,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Protein Structure and Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Institute of Genetics; Japan Society for the Promotion of Science; Research Organization of Information and Systems","keywords":"Modal; Molecular dynamics; Computer science; Dynamics (music); Biological system; Physics; Statistical physics; Classical mechanics; Chemistry; Biology; Acoustics; Quantum mechanics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001084901,0.001275208,0.000832956,0.001740651,0.0009034916,0.001343266,0.002538377,0.001180696,0.01648263],"category_scores_gemma":[0.005909528,0.0005151188,0.0008068233,0.001333477,0.0004920747,0.001217623,0.001479865,0.001340301,0.005012929],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007303373,"about_ca_system_score_gemma":0.001611691,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004023919,"about_ca_topic_score_gemma":0.003825238,"domain_scores_codex":[0.9994389,0.00008615355,0.00005560031,0.000152827,0.0002173993,0.0000489936],"domain_scores_gemma":[0.9981428,0.0007745456,0.0002435429,0.0002753149,0.0004125072,0.0001512332],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001197587,0.0005221121,0.02808991,0.003273924,0.0005158837,0.0008197277,0.001209199,0.2281116,0.0622434,0.0368774,0.2612737,0.3758656],"study_design_scores_gemma":[0.00006191913,0.00004157474,0.003036239,0.0000661134,0.00003101467,0.0001210984,0.00004386054,0.9370213,0.02487435,0.005164881,0.02943926,0.00009827672],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04924817,0.0005573524,0.7294459,0.0004608869,0.0002712937,0.0004020573,0.02816947,0.1863566,0.005088252],"genre_scores_gemma":[0.2661092,0.0005101747,0.6699317,0.0002356644,0.0001186491,0.001502166,0.03986952,0.01851138,0.003211666],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01648263,"threshold_uncertainty_score":0.05513984,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007779964906881822,"score_gpt":0.2313712429815616,"score_spread":0.2235912780746798,"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."}}