{"id":"W4403909101","doi":"10.1016/j.jsb.2024.108148","title":"Vesicle Picker: A tool for efficient identification of membrane protein complexes in vesicles","year":2024,"lang":"en","type":"article","venue":"Journal of Structural Biology","topic":"Cellular transport and secretion","field":"Biochemistry, Genetics and Molecular Biology","cited_by":9,"is_retracted":false,"has_abstract":false,"ca_institutions":"Hospital for Sick Children; University of Toronto","funders":"","keywords":"Vesicle; Identification (biology); Chemistry; Membrane; Lipid vesicle; Biophysics; Computational biology; Nanotechnology; Biochemistry; Biology; Materials science","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":[0.00025569,0.00007343029,0.0001549035,0.00008363488,0.00001814445,0.000009883654,0.000100209,0.00008880776,0.000009247291],"category_scores_gemma":[0.00003260949,0.00005631937,0.000113681,0.00006996249,0.00006807853,0.000004397285,0.00001050875,0.00006514252,4.134148e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001184614,"about_ca_system_score_gemma":0.00004544485,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006574837,"about_ca_topic_score_gemma":0.000008098685,"domain_scores_codex":[0.9992299,0.00003210393,0.0004581239,0.0001210784,0.00005428102,0.0001045076],"domain_scores_gemma":[0.9996521,0.00001365682,0.0001525567,0.00007712279,0.00008492854,0.0000195845],"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":[0.0002131065,0.00001069222,0.00119713,0.00008564982,0.00003091219,0.000002177241,0.00007483036,0.0001458124,0.9951737,0.0009643417,0.000006012131,0.002095646],"study_design_scores_gemma":[0.0004447734,0.0003621494,0.01401389,0.0000396694,0.00001825594,0.00003238382,0.00003854861,0.0006911734,0.9817385,0.00105011,0.001496954,0.00007359408],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.992179,0.001779484,0.005456092,0.0001468798,0.0002176786,0.0001846732,0.00002931136,0.000002498147,0.000004414502],"genre_scores_gemma":[0.9991038,0.00003054715,0.0004925491,0.000009529605,0.0001419238,0.000004235258,0.0001895826,0.000006753024,0.00002112408],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01343519,"threshold_uncertainty_score":0.2296637,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00973929390352222,"score_gpt":0.2601834255041052,"score_spread":0.250444131600583,"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."}}