{"id":"W4315491203","doi":"10.1007/s44163-022-00046-0","title":"Leveraging machine learning and blockchain in E-commerce and beyond: benefits, models, and application","year":2023,"lang":"en","type":"article","venue":"Discover Artificial Intelligence","topic":"Blockchain Technology Applications and Security","field":"Computer Science","cited_by":64,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Ryerson University","keywords":"Blockchain; Computer science; Machine learning; Artificial intelligence; Data science; Big data; Reliability (semiconductor); Computer security; Data mining","routes":{"ca_aff":true,"ca_fund":true,"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":[0.003883003,0.0005793353,0.0006932714,0.001054615,0.000747842,0.003031231,0.0009321981,0.001485187,0.003303705],"category_scores_gemma":[0.008019183,0.0003396983,0.0004731906,0.001946609,0.001871404,0.004642088,0.001344731,0.00163147,0.0003789089],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001803067,"about_ca_system_score_gemma":0.001413897,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008415922,"about_ca_topic_score_gemma":0.006087439,"domain_scores_codex":[0.9987074,0.0007906231,0.00004957112,0.0001222164,0.0002214463,0.0001087682],"domain_scores_gemma":[0.9908459,0.007359549,0.0004884864,0.0003801576,0.0007101588,0.00021582],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002736347,0.0003581117,0.009801935,0.0002159314,0.00008384747,0.0002803704,0.0002527627,0.5479839,0.0006677732,0.3724214,0.002717727,0.06494261],"study_design_scores_gemma":[0.00001377465,0.00004186117,0.0004019205,0.00004516962,0.00001058816,0.00002605771,0.00005302933,0.8896866,0.0002114081,0.1079363,0.001560191,0.00001308718],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4114797,0.01656483,0.4950778,0.01576091,0.0003966118,0.0003109651,0.0004226052,0.000487397,0.05949914],"genre_scores_gemma":[0.9766495,0.003342999,0.01673988,0.0001269813,0.0001224084,0.00006473692,0.00008272716,0.00001634862,0.002854405],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008415922,"threshold_uncertainty_score":0.02053553,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02991186868938587,"score_gpt":0.2738660497949454,"score_spread":0.2439541811055596,"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."}}