{"id":"W3129932070","doi":"10.1038/s41467-021-21509-5","title":"Local computational methods to improve the interpretability and analysis of cryo-EM maps","year":2021,"lang":"en","type":"article","venue":"Nature Communications","topic":"Advanced Electron Microscopy Techniques and Applications","field":"Biochemistry, Genetics and Molecular Biology","cited_by":78,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; Ministerio de Ciencia e Innovación","keywords":"Interpretability; Cryo-electron microscopy; Sharpening; Computer science; Resolution (logic); Transformation (genetics); Data mining; Artificial intelligence; Physics; Biology; Nuclear magnetic resonance","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":[0.002563911,0.001280374,0.001210312,0.001430375,0.0007384064,0.001911063,0.002614936,0.001147709,0.006055056],"category_scores_gemma":[0.007717471,0.0006259095,0.001263343,0.001192605,0.000804627,0.001767648,0.001805772,0.001780311,0.001945796],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008121182,"about_ca_system_score_gemma":0.001293449,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002452115,"about_ca_topic_score_gemma":0.003942752,"domain_scores_codex":[0.9992636,0.0002874303,0.00005693235,0.0001437505,0.000205005,0.00004326429],"domain_scores_gemma":[0.9972127,0.00143661,0.0002062648,0.0005610153,0.0004998779,0.0000835595],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000328846,0.0002257437,0.001971846,0.001063031,0.0002836865,0.0003046191,0.0004419169,0.624722,0.03795101,0.06278204,0.007342114,0.2625831],"study_design_scores_gemma":[0.00002683993,0.00002509143,0.0001847788,0.00002216465,0.00002208561,0.00003696828,0.00003129887,0.97874,0.004432003,0.0137421,0.00272268,0.00001405704],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009469613,0.0001904539,0.9862264,0.000138924,0.0000357881,0.00004546634,0.0001507533,0.002698845,0.001043812],"genre_scores_gemma":[0.07203875,0.0003130597,0.9237409,0.0001140405,0.00003758639,0.0002461463,0.0005276456,0.001894341,0.001087506],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006055056,"threshold_uncertainty_score":0.02025616,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00798819315124865,"score_gpt":0.3964573508807209,"score_spread":0.3884691577294722,"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."}}