{"id":"W4412510372","doi":"10.1149/ma2025-01182mtgabs","title":"Autoeis: Automated Bayesian Model Selection and Analysis for Electrochemical Impedance Spectroscopy","year":2025,"lang":"en","type":"article","venue":"ECS Meeting Abstracts","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada; University of Toronto","funders":"","keywords":"Dielectric spectroscopy; Selection (genetic algorithm); Bayesian probability; Computer science; Model selection; Materials science; Analytical Chemistry (journal); Artificial intelligence; Electrochemistry; Chemistry; Chromatography; Electrode","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":[0.0002261484,0.0001574385,0.000253338,0.0002136268,0.0001140504,0.00007875275,0.00006608559,0.0001249052,0.000001651453],"category_scores_gemma":[0.00009887113,0.0001721347,0.00009210423,0.0005100528,0.00001006321,0.0000650058,0.000006774057,0.0001526689,0.000002229897],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001139748,"about_ca_system_score_gemma":0.00002883523,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002951221,"about_ca_topic_score_gemma":0.0000506338,"domain_scores_codex":[0.9990484,0.00001364046,0.0003054305,0.0002373206,0.00009104617,0.0003041213],"domain_scores_gemma":[0.9996448,0.00008246549,0.00005234509,0.0001091311,0.00004468764,0.00006656626],"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":[0.00001470082,0.00000650516,0.0002226057,0.0000539836,0.0001875899,2.186358e-7,0.000030011,0.5018833,0.497234,0.00001117084,0.0002704826,0.00008546493],"study_design_scores_gemma":[0.0002467539,0.00001226245,0.000918199,0.00002621717,0.0001311866,0.000001808938,0.00001370037,0.7436856,0.2545808,0.0001081211,0.0001643319,0.0001110055],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8648661,0.0003842627,0.11509,0.0001138198,0.0002243353,0.0003208095,0.000006974825,0.002669833,0.01632388],"genre_scores_gemma":[0.9951511,0.00001158444,0.004489162,0.00003081473,0.00005988143,0.00006213702,0.000008686382,0.0000202749,0.0001663575],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2426532,"threshold_uncertainty_score":0.7019448,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005057700832058521,"score_gpt":0.2402219554610262,"score_spread":0.2351642546289676,"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."}}