{"id":"W2265299935","doi":"10.1016/j.str.2019.01.005","title":"De Novo Structural Pattern Mining in Cellular Electron Cryotomograms","year":2019,"lang":"en","type":"article","venue":"Structure","topic":"Advanced Electron Microscopy Techniques and Applications","field":"Biochemistry, Genetics and Molecular Biology","cited_by":59,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"National Institute of General Medical Sciences; National Institutes of Health; Arnold and Mabel Beckman Foundation; Howard Hughes Medical Institute","keywords":"Template; Proteome; Proteomics; Computer science; Visualization; Computational biology; Electron tomography; Resolution (logic); Data mining; Pattern recognition (psychology); Artificial intelligence; Bioinformatics; Nanotechnology; Chemistry; Biology; Materials science; 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":[0.00003071088,0.0001370019,0.0001049299,0.00003046509,0.00003506399,0.000018932,0.0001742935,0.0001526282,0.00007174952],"category_scores_gemma":[0.000004326801,0.0001321831,0.00004221379,0.00009718537,0.00002412844,0.000003434894,0.00004610177,0.0001581376,0.000002467328],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003324755,"about_ca_system_score_gemma":0.00003872354,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001596237,"about_ca_topic_score_gemma":0.00006566157,"domain_scores_codex":[0.9991671,0.0000163998,0.000121325,0.0002946621,0.00005716002,0.0003433484],"domain_scores_gemma":[0.9996138,0.000002838217,0.0000472897,0.0002768504,0.00001985021,0.00003933018],"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":[0.0000130743,0.000003336169,0.0284931,0.000007987274,0.000005512051,0.000001190279,0.00002939827,0.00008334904,0.9680042,0.0000892469,0.00007451023,0.003195069],"study_design_scores_gemma":[0.0002650313,0.0001310931,0.005401,0.000007659794,0.000003846972,0.00002476451,0.0000261351,0.00009530759,0.9827548,0.001035531,0.01006427,0.0001905953],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9944563,0.0002781532,0.004722389,0.0000464955,0.00003657832,0.0002338436,0.00001941147,0.00002165431,0.000185183],"genre_scores_gemma":[0.9954712,0.00001746855,0.003660453,0.0002537343,0.0000989211,0.00001533407,0.0001837733,0.00002447001,0.0002746407],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0230921,"threshold_uncertainty_score":0.5390271,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.002391457355147626,"score_gpt":0.2712922705819407,"score_spread":0.268900813226793,"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."}}