{"id":"W3185579844","doi":"10.1007/978-3-030-82427-3_1","title":"A High-Resolution Model of the Human Entorhinal Cortex in the ‘BigBrain’ – Use Case for Machine Learning and 3D Analyses","year":2021,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Cell Image Analysis Techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Montreal Neurological Institute and Hospital; National Research Council Canada","funders":"Horizon 2020 Framework Programme; European Commission","keywords":"Entorhinal cortex; Computer science; Data set; Set (abstract data type); High resolution; Pattern recognition (psychology); Artificial intelligence; Visual cortex; Neuroscience; Hippocampus; Biology; Geology; Remote sensing","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":[],"consensus_categories":[],"category_scores_codex":[0.000474275,0.0001829556,0.0002240228,0.0001401052,0.0001535889,0.00009145196,0.0003983828,0.0001449683,0.000002287057],"category_scores_gemma":[0.0001457089,0.0001206547,0.0001117354,0.0001418519,0.0003834178,0.000009507738,0.0003812371,0.0002756888,3.412221e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002746541,"about_ca_system_score_gemma":0.00008506612,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002527437,"about_ca_topic_score_gemma":0.001783003,"domain_scores_codex":[0.9988267,0.00005711367,0.0002427849,0.000502057,0.000204485,0.0001668725],"domain_scores_gemma":[0.9991326,0.00009510331,0.0001765154,0.0004529388,0.0001217355,0.00002105519],"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":[0.00003110111,0.00008159458,0.001013076,0.00009481978,0.00008228777,0.000129718,0.000607981,0.1430933,0.8175796,0.001051058,0.00004537416,0.03619013],"study_design_scores_gemma":[0.0003322557,0.0002316326,0.0001651785,0.0001476644,0.0001117276,0.0002485925,0.000001870809,0.9079953,0.08580738,0.0039769,0.0006158785,0.0003655859],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08393407,0.0007855419,0.9148014,0.00009386517,0.00002508126,0.0002697911,0.0000109631,0.000006207653,0.00007310015],"genre_scores_gemma":[0.9692362,0.00007364131,0.03001157,0.0003102949,0.00007174907,0.000007996035,0.00004339293,0.00001255017,0.000232596],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8853021,"threshold_uncertainty_score":0.4920155,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02865224370272627,"score_gpt":0.3041817925893112,"score_spread":0.2755295488865849,"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."}}