{"id":"W2339949171","doi":"10.1007/978-0-387-31701-4_3","title":"Modeling Electrochemical Phenomena via Markov Chains and Processes","year":2007,"lang":"en","type":"book-chapter","venue":"Modern aspects of electrochemistry","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Markov chain; Examples of Markov chains; Markov model; Markov process; Variable-order Markov model; Balance equation; Computer science; Markov renewal process; Markov property; Statistical physics; Mathematics; Statistics; Machine learning; Physics","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"],"consensus_categories":[],"category_scores_codex":[0.00009233099,0.0005007907,0.0005172931,0.00009703993,0.0001183056,0.00007218359,0.0009254325,0.0003837407,0.0000148264],"category_scores_gemma":[0.000016704,0.0004729974,0.0001147019,0.0001291916,0.0001003717,0.0001270518,0.0002787487,0.0007000688,0.000003241191],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009786544,"about_ca_system_score_gemma":0.0002465872,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002662804,"about_ca_topic_score_gemma":0.000008966142,"domain_scores_codex":[0.9975572,0.000003832206,0.000480947,0.0009438274,0.0004243489,0.0005898711],"domain_scores_gemma":[0.9984649,0.00007403055,0.0002580819,0.0007402307,0.0002591703,0.0002036313],"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":[0.00005695582,0.0001362296,0.000001032126,0.0009549909,0.0002118284,0.00003758087,0.0001300936,0.000204763,0.7746138,0.1837762,0.0001476344,0.03972885],"study_design_scores_gemma":[0.0003437843,0.0001197869,2.672555e-7,0.0002545266,0.00007121918,0.0002265143,0.00000186621,0.4044847,0.2656527,0.3266081,0.001198433,0.00103819],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0004111188,0.004444368,0.7386799,0.0002661805,0.00001945106,0.0002370071,0.00000457935,0.0001852065,0.2557521],"genre_scores_gemma":[0.9461473,0.00150831,0.007919217,0.0002301593,0.0007090049,0.00003731625,0.00007023987,0.0001239292,0.04325456],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9457362,"threshold_uncertainty_score":0.9997722,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01307072221414322,"score_gpt":0.2257765957540298,"score_spread":0.2127058735398866,"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."}}