{"id":"W4233138705","doi":"10.1002/9783527808465.emc2016.6922","title":"<scp>3D</scp> mapping of subcellular structures with super‐resolution array tomography","year":2016,"lang":"en","type":"other","venue":"European Microscopy Congress 2016: Proceedings","topic":"Genetics, Aging, and Longevity in Model Organisms","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lunenfeld-Tanenbaum Research Institute; University of Toronto; Mount Sinai Hospital","funders":"","keywords":"Connectomics; Microscopy; Resolution (logic); Context (archaeology); Tomography; Electron tomography; Electron microscope; Image resolution; Connectome; Computer science; Optics; Physics; Artificial intelligence; Materials science; Computer vision; Biology; Scanning transmission electron microscopy; Neuroscience","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003061923,0.0007374603,0.0005749269,0.0003645112,0.0001227021,0.0001189991,0.0008328105,0.0004007424,0.0001830898],"category_scores_gemma":[0.00006194832,0.0005921822,0.0002000552,0.0001479977,0.0006515829,0.00001204011,0.0002335527,0.0002736776,0.0001062831],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001998629,"about_ca_system_score_gemma":0.0001009938,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000290002,"about_ca_topic_score_gemma":0.00001164559,"domain_scores_codex":[0.997363,0.00007206246,0.0004834209,0.001044014,0.000350902,0.000686578],"domain_scores_gemma":[0.9982175,0.00001052922,0.0007132675,0.0005446103,0.0003145122,0.0001995612],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002170921,0.00003818921,0.001934152,0.0003195627,0.0002265694,0.000005923796,0.0003224421,0.000001173747,0.7395232,0.00003452051,0.2574697,0.0001028547],"study_design_scores_gemma":[0.0007275997,0.0002532536,0.0002922412,0.00052209,0.00008999449,0.00002420798,0.0001115235,0.000001529595,0.448759,0.00001464678,0.5488884,0.0003154245],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.3393063,0.02038495,0.01962554,0.00006392402,0.002422427,0.002075776,0.0007266794,0.0004327011,0.6149617],"genre_scores_gemma":[0.2294645,0.004531016,0.02526561,0.0003964302,0.003658432,0.00003773736,0.0005561866,0.002787292,0.7333028],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.2914187,"threshold_uncertainty_score":0.999653,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006229560848076209,"score_gpt":0.2099933481917478,"score_spread":0.2037637873436716,"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."}}