{"id":"W2901020795","doi":"10.3389/fncir.2018.00094","title":"A Pipeline for Volume Electron Microscopy of the Caenorhabditis elegans Nervous System","year":2018,"lang":"en","type":"article","venue":"Frontiers in Neural Circuits","topic":"Genetics, Aging, and Longevity in Model Organisms","field":"Biochemistry, Genetics and Molecular Biology","cited_by":55,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hospital for Sick Children; University of Toronto; Lunenfeld-Tanenbaum Research Institute; SickKids Foundation; Mount Sinai Hospital","funders":"National Institute of Neurological Disorders and Stroke; Medical Research Council; National Institute of General Medical Sciences; Canadian Institutes of Health Research; National Institutes of Health; Human Frontier Science Program","keywords":"Caenorhabditis elegans; Neuroscience; Nervous system; Central nervous system; Biology; Pipeline (software); Volume (thermodynamics); Computer science; Physics; Genetics","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":[0.004057077,0.001754875,0.001384171,0.003264962,0.001568426,0.002652932,0.002781388,0.001430931,0.02461898],"category_scores_gemma":[0.00480993,0.002006742,0.001654385,0.001292176,0.0006356736,0.002762494,0.003541331,0.003353759,0.01156234],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001116708,"about_ca_system_score_gemma":0.002203385,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002841395,"about_ca_topic_score_gemma":0.006161649,"domain_scores_codex":[0.9988876,0.0001316035,0.00009874666,0.0002172552,0.0005262858,0.0001384762],"domain_scores_gemma":[0.9975069,0.0005064429,0.0001260078,0.0006021056,0.0009221866,0.0003363752],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0006424696,0.0001678815,0.003933799,0.002153578,0.0004818244,0.0008013289,0.0008072149,0.008077628,0.3022414,0.02565945,0.1892267,0.4658067],"study_design_scores_gemma":[0.0002403662,0.0003811274,0.009139353,0.0007684677,0.0002680841,0.002539754,0.0002855442,0.08945385,0.1447888,0.05276746,0.6987973,0.000569864],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005266323,0.001942465,0.9202273,0.0008925482,0.000423214,0.0005156451,0.008103812,0.05491724,0.007711535],"genre_scores_gemma":[0.01706296,0.001966681,0.955228,0.0003903861,0.0001036291,0.0008593493,0.01135784,0.008499165,0.004531907],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02461898,"threshold_uncertainty_score":0.08235872,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006309001296173258,"score_gpt":0.2283168114856343,"score_spread":0.222007810189461,"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."}}