{"id":"W2009465944","doi":"10.1021/cn200094j","title":"Label-Free Visualization of Ultrastructural Features of Artificial Synapses via Cryo-EM","year":2011,"lang":"en","type":"article","venue":"ACS Chemical Neuroscience","topic":"Photosynthetic Processes and Mechanisms","field":"Biochemistry, Genetics and Molecular Biology","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Montreal Neurological Institute and Hospital","funders":"Canadian Institutes of Health Research; Fonds Québécois de la Recherche sur la Nature et les Technologies; Natural Sciences and Engineering Research Council of Canada; Molson Foundation; McGill University","keywords":"Ultrastructure; Synaptic vesicle; Hippocampal formation; Electron microscope; Biophysics; Cryo-electron microscopy; Synapse; Vesicle; Membrane; Chemistry; Nanotechnology; Neuroscience; Materials science; Biology; Anatomy; Biochemistry; Physics","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":[0.0001818239,0.0002297206,0.0001739203,0.0001779318,0.0002570251,0.0004175587,0.0004391363,0.000461482,0.001120717],"category_scores_gemma":[0.0002410208,0.0002403038,0.0001757829,0.0001325467,0.0004691399,0.0005257677,0.0004740208,0.0008288943,0.0005200783],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003343624,"about_ca_system_score_gemma":0.000166803,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003866957,"about_ca_topic_score_gemma":0.000874299,"domain_scores_codex":[0.9999133,0.00001049448,0.000008158357,0.00002021409,0.00003141713,0.00001643405],"domain_scores_gemma":[0.9997905,0.00006347834,0.00003990971,0.00005195334,0.00003240092,0.00002178807],"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":[0.00001875685,0.000006360196,0.0001107304,0.00004415524,0.000005674485,0.00007861096,0.00001643087,0.0001654195,0.998341,0.000476136,0.00003686249,0.0006998488],"study_design_scores_gemma":[0.00001889513,0.00008645933,0.007605589,0.00002569906,0.00002153952,0.001194072,0.00004626294,0.006154572,0.9770321,0.000628658,0.007171375,0.00001487698],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8978859,0.001512191,0.09359907,0.0002591328,0.0001138599,0.00005504768,0.0007774889,0.0003269198,0.005470428],"genre_scores_gemma":[0.9153536,0.001502587,0.07912459,0.0001090262,0.00002746513,0.00008607334,0.0008218132,0.0001153057,0.002859401],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001120717,"threshold_uncertainty_score":0.003749192,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01687842279721537,"score_gpt":0.2538160142030031,"score_spread":0.2369375914057878,"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."}}