{"id":"W2884672152","doi":"10.1201/b17306","title":"Computational and Visualization Techniques for Structural Bioinformatics Using Chimera","year":2014,"lang":"en","type":"book","venue":"","topic":"Genetics, Bioinformatics, and Biomedical Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Python (programming language); Chimera (genetics); Visualization; Scripting language; Computer science; Computation; Structural bioinformatics; Computational biology; Bioinformatics; Computational science; Data mining; Biology; Algorithm; Programming language; Protein structure; Genetics; Biochemistry","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":[0.0002495015,0.0002534873,0.0002508297,0.0001326995,0.000132735,0.00008233716,0.0001725656,0.000507181,0.00001774251],"category_scores_gemma":[0.0001013735,0.0002143537,0.00009491567,0.0000301152,0.0002419138,0.000005394194,0.00017613,0.000103574,0.000003538686],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003222225,"about_ca_system_score_gemma":0.0003243875,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000027311,"about_ca_topic_score_gemma":0.000005414564,"domain_scores_codex":[0.9987345,0.00001659437,0.0004618582,0.0002230887,0.0003020595,0.0002619353],"domain_scores_gemma":[0.9991453,0.0000350012,0.0001963686,0.0001876778,0.000298424,0.0001371802],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0004940447,0.0001101254,0.0002621698,0.008906579,0.00125259,0.000001935239,0.0006241296,0.0006916072,0.01526602,0.02280848,0.5435872,0.4059951],"study_design_scores_gemma":[0.0008228262,0.001016765,0.00003196513,0.0001470909,0.00008827444,0.00004175503,0.00004140035,0.3590368,0.008525578,0.008334884,0.6210936,0.0008190835],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001416501,0.0004407343,0.9443662,0.00008502758,0.0002742982,0.00156719,0.000330473,0.00006330937,0.05145633],"genre_scores_gemma":[0.004033395,0.0009711124,0.6815282,0.002208307,0.002626487,0.00006001162,0.02031671,0.0001864137,0.2880694],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.405176,"threshold_uncertainty_score":0.8741092,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01909083510337069,"score_gpt":0.3232759670534511,"score_spread":0.3041851319500805,"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."}}