{"id":"W2960528273","doi":"10.1523/eneuro.0425-18.2019","title":"Characterization of Nanoscale Organization of F-Actin in Morphologically Distinct Dendritic Spines<i>In Vitro</i>Using Supervised Learning","year":2019,"lang":"en","type":"article","venue":"eNeuro","topic":"Cell Image Analysis Techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of General Medical Sciences; Canadian Institutes of Health Research; University Grants Commission; Tata Trusts; McGill University; Department of Biotechnology, Ministry of Science and Technology, India; Science and Engineering Research Board; National Institutes of Health; Government of Canada; Indian Institute of Science","keywords":"Dendritic spine; Cytoskeleton; Biology; Neuroscience; Microtubule; Cytoarchitecture; Actin; Ultrastructure; Nanoscopic scale; Cell biology; Nanotechnology; Anatomy; Cell; Materials science; Biochemistry","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001938437,0.000190178,0.0002157951,0.0002622641,0.0001733776,0.0002939059,0.0002631497,0.0003346264,0.0003218223],"category_scores_gemma":[0.0003709813,0.0001207546,0.0002267267,0.0001994958,0.0002878716,0.0002573146,0.0001536636,0.0003179868,0.0001748448],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003396384,"about_ca_system_score_gemma":0.0002378828,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000967668,"about_ca_topic_score_gemma":0.002072026,"domain_scores_codex":[0.9998749,0.00001032342,0.000007282423,0.00004616181,0.00004485735,0.00001648137],"domain_scores_gemma":[0.9997298,0.0000741265,0.00007976653,0.00003551859,0.00006162939,0.0000191353],"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":[0.00004417741,0.00005996176,0.002628127,0.00006387207,0.00001118691,0.00004945013,0.00003544593,0.007311323,0.9736405,0.0002252476,0.00008560765,0.01584512],"study_design_scores_gemma":[0.000006751095,0.0001956437,0.02287029,0.000009166571,0.00001712061,0.0002373472,0.0000483057,0.2274616,0.7478575,0.0004492766,0.0008288142,0.00001825128],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8896436,0.0002707339,0.1080379,0.000071494,0.00001384994,0.00004081324,0.000348251,0.0002982549,0.001275054],"genre_scores_gemma":[0.9179876,0.0003375957,0.08040188,0.00003202782,0.00001107564,0.00005480179,0.0004974742,0.00004544255,0.0006320184],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.000967668,"threshold_uncertainty_score":0.002464294,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006078874819825165,"score_gpt":0.2252424325312794,"score_spread":0.2191635577114543,"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."}}