{"id":"W4416075487","doi":"10.34218/ijcet_16_06_001","title":"COMPLIANCE-AWARE MACHINE LEARNING PIPELINES: ANALYTICAL MODELLING OF REGULATORY CONSTRAINTS IN AUTOMATED DECISION SYSTEMS","year":2025,"lang":"","type":"article","venue":"INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING & TECHNOLOGY","topic":"Petri Nets in System Modeling","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Decision system; Decision support system; Feature (linguistics); Automation; Decision analysis; Decision tree","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":[0.00156415,0.000472654,0.001299237,0.005642027,0.00005343801,0.0002126068,0.00342719,0.0005485712,0.000006375467],"category_scores_gemma":[0.0002890519,0.0005057997,0.0002873694,0.00198282,0.0002372166,0.000481102,0.0009579367,0.001529372,0.000004826316],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007574362,"about_ca_system_score_gemma":0.0004732736,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002333638,"about_ca_topic_score_gemma":0.000001151841,"domain_scores_codex":[0.9942734,0.0001302212,0.003358016,0.0005741325,0.001135394,0.0005288537],"domain_scores_gemma":[0.995073,0.0006463575,0.001318361,0.0005684931,0.002277212,0.0001166065],"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":[0.0000627913,0.0001389715,0.001928388,0.0001755828,0.0004097355,0.0003812507,0.0001204691,0.9598642,0.0001707803,0.008614951,0.00006263927,0.02807027],"study_design_scores_gemma":[0.00171042,0.0001577896,0.0002614913,0.01119448,0.00003760659,0.00104863,0.00003842921,0.9842649,0.0002130281,0.0003630125,0.0004037246,0.0003065593],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04889932,0.004191002,0.9379794,0.0004163173,0.008022283,0.0001871786,0.000009464538,0.0002589785,0.00003603736],"genre_scores_gemma":[0.9169567,0.0002502547,0.08247677,0.00002299998,0.0002395325,0.000003243445,0.000002122224,0.00002829314,0.0000200549],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8680574,"threshold_uncertainty_score":0.9997393,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02404041738498901,"score_gpt":0.2820826967703741,"score_spread":0.2580422793853851,"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."}}