{"id":"W4389232540","doi":"10.1109/icjece.2023.3320103","title":"Electrocardiogram Analysis for Kratom Users Utilizing Deep Residual Learning Network and Machine Learning","year":2023,"lang":"en","type":"article","venue":"Canadian Journal of Electrical and Computer Engineering","topic":"Alkaloids: synthesis and pharmacology","field":"Pharmacology, Toxicology and Pharmaceutics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Thai Health Promotion Foundation","keywords":"Artificial intelligence; Machine learning; Extractor; Deep learning; Residual; Computer science; Engineering; Algorithm","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003536998,0.0003676467,0.0003402024,0.0004351636,0.00008695102,0.0002873002,0.0002026406,0.0003395229,0.0009970118],"category_scores_gemma":[0.001069474,0.00009147524,0.0002725033,0.0002903961,0.00009716061,0.0002168217,0.000200608,0.0003717529,0.0002320757],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001521414,"about_ca_system_score_gemma":0.0001269043,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001493246,"about_ca_topic_score_gemma":0.002360519,"domain_scores_codex":[0.9998541,0.00003762164,0.00001387982,0.00004815668,0.0000320435,0.00001425642],"domain_scores_gemma":[0.9997212,0.0001242644,0.00005309914,0.00002349974,0.00006073812,0.00001702586],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002690832,0.0007154276,0.3835986,0.0004854894,0.0004320437,0.001559511,0.0004014927,0.03092722,0.1156613,0.0005290309,0.002307819,0.4606912],"study_design_scores_gemma":[0.00006808052,0.00189954,0.4413852,0.00006673902,0.0002884614,0.002402316,0.0002946272,0.5189623,0.03172937,0.0008416003,0.001991677,0.00007010437],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9307932,0.0007304586,0.0655881,0.0002327896,0.00004499725,0.00009158592,0.0007819185,0.0004161266,0.001320705],"genre_scores_gemma":[0.9783334,0.0004405256,0.01941342,0.00005617002,0.00002153889,0.00005636352,0.0005433254,0.00001985327,0.001115418],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001493246,"threshold_uncertainty_score":0.003335297,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0467857233595495,"score_gpt":0.3114793259344913,"score_spread":0.2646936025749418,"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."}}