{"id":"W4362585011","doi":"10.48084/etasr.5683","title":"Multi-level Association Rule Mining for the Discovery of Strong Underrepresented Patterns","year":2023,"lang":"en","type":"article","venue":"Engineering Technology & Applied Science Research","topic":"Data Mining Algorithms and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"International Development Research Centre; Styrelsen för Internationellt Utvecklingssamarbete","keywords":"Tanzania; Production (economics); Business; Dairy farming; Agriculture; Milk production; Agricultural science; Population; Association rule learning; Cluster (spacecraft); Agricultural economics; Geography; Environmental health; Economics; Environmental science; Environmental planning; Biology; Animal science; Medicine; Computer science; Data mining","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"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":[0.009303084,0.001447977,0.003031633,0.009439099,0.001518774,0.002999688,0.00273412,0.001640081,0.001476185],"category_scores_gemma":[0.03182368,0.0008788367,0.003474629,0.007523662,0.0008437072,0.002586097,0.002295152,0.002646676,0.001234347],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007731672,"about_ca_system_score_gemma":0.003134922,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004656531,"about_ca_topic_score_gemma":0.007087957,"domain_scores_codex":[0.9893141,0.003081077,0.002225128,0.002801816,0.002080139,0.0004977432],"domain_scores_gemma":[0.9650773,0.02564218,0.003599983,0.002416832,0.002787274,0.0004764977],"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":[0.0008912461,0.00141145,0.1698966,0.002750245,0.003232733,0.003268685,0.001951891,0.09259983,0.01133007,0.008909321,0.007719119,0.6960388],"study_design_scores_gemma":[0.0001348198,0.0005179771,0.02018295,0.0006388628,0.0009375436,0.002028843,0.001172626,0.9133263,0.007703097,0.04309294,0.01012751,0.0001365139],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.09110335,0.003373683,0.8945065,0.001168997,0.0001623514,0.0007560166,0.004494464,0.002822266,0.001612378],"genre_scores_gemma":[0.3828109,0.0009298022,0.6080619,0.0004269976,0.00009363002,0.000750793,0.006090411,0.00008200621,0.0007536514],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009439099,"threshold_uncertainty_score":0.04919994,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1066213340430877,"score_gpt":0.3716883813356935,"score_spread":0.2650670472926058,"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."}}