{"id":"W4366204133","doi":"10.1101/2023.04.17.537196","title":"mEMbrain: an interactive deep learning MATLAB tool for connectomic segmentation on commodity desktops","year":2023,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lunenfeld-Tanenbaum Research Institute; University of Toronto; Mount Sinai Hospital","funders":"National Institutes of Health","keywords":"Computer science; MATLAB; Segmentation; Commodity; Deep learning; Artificial intelligence; Human–computer interaction; Business; Operating system","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","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.001010911,0.0004603929,0.000467734,0.0003747604,0.0003259018,0.001051953,0.00130855,0.0002870217,0.00001850928],"category_scores_gemma":[0.0006073161,0.0005142103,0.0001436414,0.0005291059,0.00005632118,0.0007411606,0.0007506333,0.0006229097,0.0001346209],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003731555,"about_ca_system_score_gemma":0.0002764068,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002266418,"about_ca_topic_score_gemma":0.00000717804,"domain_scores_codex":[0.9970702,0.0003235945,0.0005402567,0.001200884,0.0004079147,0.0004571843],"domain_scores_gemma":[0.9970325,0.0003739704,0.0005679069,0.001288507,0.0005400993,0.0001970072],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006489044,0.003426314,0.01454278,0.004552625,0.002391389,0.0003013432,0.001772585,0.0590102,0.5777125,0.3247803,0.01008155,0.0007795373],"study_design_scores_gemma":[0.001481234,0.000360556,0.01669088,0.0005022643,0.00011108,2.638634e-8,0.00003774188,0.8285947,0.1460152,0.00009102969,0.004556192,0.001559013],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1870277,0.00002467139,0.8082831,0.0003316437,0.00165557,0.0009875298,0.0003115149,0.001372583,0.000005653792],"genre_scores_gemma":[0.9727084,0.00005374392,0.02584228,0.0006861369,0.000297997,0.0002654172,0.00001742517,0.0001117499,0.00001686441],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7856807,"threshold_uncertainty_score":0.999985,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0349117459481966,"score_gpt":0.2916420342239535,"score_spread":0.2567302882757568,"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."}}