{"id":"W4403905564","doi":"10.59934/jaiea.v4i1.626","title":"Application of Apriori to Determine Correlations between Source Competencies Human Resources with Education and Working Period","year":2024,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence and Engineering Applications (JAIEA)","topic":"Technology-Enhanced Education Studies","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Period (music); A priori and a posteriori; Computer science; Data science","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":[0.01127726,0.00235081,0.001960056,0.008439332,0.0008902107,0.002639259,0.001552598,0.0009188483,0.003661828],"category_scores_gemma":[0.02652727,0.0008219206,0.002737602,0.006091679,0.0005182459,0.001442647,0.001083095,0.001957506,0.001176019],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000574972,"about_ca_system_score_gemma":0.002941575,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002435796,"about_ca_topic_score_gemma":0.00202513,"domain_scores_codex":[0.9951459,0.001733989,0.0009641332,0.001129939,0.0007688368,0.0002572963],"domain_scores_gemma":[0.9700134,0.02531075,0.001851972,0.0007083242,0.001811792,0.0003037593],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.003119126,0.001621837,0.4374115,0.002469512,0.004700575,0.002920782,0.001435494,0.1092553,0.009309248,0.004677554,0.007542699,0.4155364],"study_design_scores_gemma":[0.0002637556,0.001957912,0.07555391,0.000363076,0.001599916,0.002632978,0.001637956,0.8787912,0.01594836,0.0100035,0.01102268,0.0002248598],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5002096,0.002832014,0.4708425,0.001232976,0.0002682265,0.001635841,0.0112766,0.006049376,0.005652826],"genre_scores_gemma":[0.6464193,0.0005847978,0.3414243,0.0001031917,0.00006975766,0.001463909,0.008636674,0.0001543526,0.001143635],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01127726,"threshold_uncertainty_score":0.05964053,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03672007675437607,"score_gpt":0.3212784273107711,"score_spread":0.2845583505563951,"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."}}