{"id":"W3205940441","doi":"10.59934/jaiea.v1i1.55","title":"Family Economic Correlation To Students Learning Achievment Using Apriori Method","year":2021,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence and Engineering Applications (JAIEA)","topic":"Data Mining and Machine Learning Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Apriori algorithm; Association rule learning; A priori and a posteriori; Value (mathematics); Transactional leadership; Association (psychology); Computer science; Psychology; Mathematics education; Artificial intelligence; Machine learning; Social psychology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007077799,0.0007988156,0.001022618,0.004235574,0.000741224,0.002448897,0.0009640942,0.0008067749,0.005239595],"category_scores_gemma":[0.03149284,0.0003053332,0.001675556,0.003410448,0.0006112084,0.001290457,0.001105345,0.001898729,0.0008640758],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005333481,"about_ca_system_score_gemma":0.001101491,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001943629,"about_ca_topic_score_gemma":0.001238172,"domain_scores_codex":[0.9946555,0.002116031,0.0007195749,0.0008863118,0.001225733,0.0003968768],"domain_scores_gemma":[0.9663596,0.02615827,0.002961966,0.001443588,0.002491276,0.0005853197],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0008307775,0.0006271946,0.8349655,0.0003466014,0.001030224,0.002419173,0.001364973,0.01938255,0.000932332,0.003373951,0.003276355,0.1314503],"study_design_scores_gemma":[0.0001116465,0.002370654,0.3891932,0.0004233678,0.001677741,0.004821093,0.007746835,0.5433842,0.01016356,0.02079562,0.01909666,0.0002154667],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.889192,0.001308107,0.09287705,0.001413549,0.0002835964,0.0004579879,0.002558822,0.0005035414,0.01140537],"genre_scores_gemma":[0.982161,0.0002313103,0.01496696,0.00003889186,0.00005241989,0.0001789154,0.0009415935,0.00002399079,0.001404807],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007077799,"threshold_uncertainty_score":0.03743142,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03430572548805251,"score_gpt":0.3403847677304493,"score_spread":0.3060790422423968,"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."}}