{"id":"W4396605260","doi":"10.1101/2024.04.30.591880","title":"Supervised Machine Learning for Bioelectrical Cellular Networks","year":2024,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Molecular Communication and Nanonetworks","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Artificial intelligence; Machine learning; Psychology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002935749,0.0008771648,0.001055065,0.001170758,0.0004948567,0.0008848993,0.001329289,0.001327285,0.001703678],"category_scores_gemma":[0.009219217,0.0003846667,0.0005898454,0.0008538215,0.0006251938,0.001032078,0.0008528198,0.001625408,0.0004376478],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001544862,"about_ca_system_score_gemma":0.0009664466,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005368719,"about_ca_topic_score_gemma":0.004943691,"domain_scores_codex":[0.9991524,0.0004289764,0.0000442058,0.0001838448,0.000131861,0.00005871551],"domain_scores_gemma":[0.9900146,0.007548091,0.0005527054,0.000454753,0.001289636,0.0001403984],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005148686,0.00006516257,0.001655113,0.0000721749,0.00004187393,0.00002489649,0.00003045542,0.9367558,0.000255671,0.003716016,0.001714322,0.05561707],"study_design_scores_gemma":[0.00000169387,0.00000546183,0.00005621855,0.000003596273,0.000001039733,0.000001769704,0.00000206955,0.9968956,0.00006414863,0.002884773,0.00008262346,0.000001033956],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0906119,0.001766343,0.901144,0.001333804,0.0001173427,0.0001528049,0.0008103036,0.001713263,0.002350218],"genre_scores_gemma":[0.8158383,0.0005691648,0.1764458,0.0002612251,0.0002020452,0.0003629367,0.001977323,0.000122968,0.004220316],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005368719,"threshold_uncertainty_score":0.01552594,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01079446047522113,"score_gpt":0.1982832226516902,"score_spread":0.1874887621764691,"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."}}