{"id":"W2587625522","doi":"10.1038/nmeth.4169","title":"cryoSPARC: algorithms for rapid unsupervised cryo-EM structure determination","year":2017,"lang":"en","type":"article","venue":"Nature Methods","topic":"Advanced Electron Microscopy Techniques and Applications","field":"Biochemistry, Genetics and Molecular Biology","cited_by":10710,"is_retracted":false,"has_abstract":false,"ca_institutions":"Hospital for Sick Children; York University; University of Toronto","funders":"","keywords":"Cryo-electron microscopy; Bottleneck; Computer science; Software; Algorithm; Single particle analysis; Bayesian probability; Gradient descent; Resolution (logic); Image processing; Artificial intelligence; Data mining; Image (mathematics); Artificial neural network","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003949965,0.005087429,0.002679573,0.002952484,0.003020191,0.003603995,0.01040903,0.00381831,0.02081997],"category_scores_gemma":[0.008894423,0.003665831,0.002816067,0.002876453,0.001385788,0.004565071,0.005804895,0.007771022,0.01848609],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001546654,"about_ca_system_score_gemma":0.003882695,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003833718,"about_ca_topic_score_gemma":0.009235818,"domain_scores_codex":[0.998495,0.0003104243,0.0001042668,0.000469447,0.0004953419,0.0001256295],"domain_scores_gemma":[0.9969778,0.001155546,0.0002121958,0.0009302602,0.0005680832,0.000156141],"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":[0.0009643685,0.0004074604,0.001655734,0.001292648,0.0006726416,0.0003132148,0.0005243911,0.04665644,0.03154406,0.02369755,0.2364683,0.6558032],"study_design_scores_gemma":[0.000326441,0.0001162733,0.0009166368,0.0001022007,0.0001320723,0.0004646587,0.0001493697,0.8428336,0.03861017,0.05733181,0.05884684,0.0001698182],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002953498,0.0003933088,0.9050635,0.0001925227,0.0001307626,0.0001706595,0.001566072,0.08838015,0.001149501],"genre_scores_gemma":[0.01186909,0.0002691061,0.9687839,0.0001419604,0.00004615507,0.0005266289,0.004952782,0.0118945,0.001515777],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02081997,"threshold_uncertainty_score":0.0696497,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01897084099966042,"score_gpt":0.4533618990654899,"score_spread":0.4343910580658295,"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."}}