{"id":"W4415360709","doi":"10.59934/jaiea.v5i1.1669","title":"Clustering of High-Achieving Students Based on Scores at Junior High School Level Using K-Means Algorithm","year":2025,"lang":"","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":"Cluster analysis; Variance (accounting); k-means clustering; MATLAB; Process (computing); Quality (philosophy); Educational data mining","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":[0.001178958,0.0004184314,0.0006604609,0.001110692,0.0005657534,0.0004296032,0.001536593,0.0001845506,0.0000238817],"category_scores_gemma":[0.0002802643,0.0004439909,0.0001926044,0.001578678,0.0001731567,0.000401736,0.0005523913,0.0007791646,0.00001917411],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002919698,"about_ca_system_score_gemma":0.0003184779,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002815929,"about_ca_topic_score_gemma":0.00001596227,"domain_scores_codex":[0.9965778,0.00008770556,0.001606862,0.0005960199,0.0006676145,0.000463975],"domain_scores_gemma":[0.9970032,0.0004830724,0.000847591,0.0009176384,0.000457465,0.0002910274],"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":[0.00004059593,0.0003506069,0.000439745,0.0001609325,0.0001241136,0.000004458858,0.0001966042,0.7148492,0.005385174,0.005042708,0.00005460211,0.2733513],"study_design_scores_gemma":[0.0001637557,0.0002500995,0.004583501,0.001144182,0.0001903538,0.00002820908,0.0001263508,0.9851977,0.006559386,0.0004881445,0.0009125559,0.000355741],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06164495,0.0003875389,0.9360995,0.0006996596,0.0006200923,0.0003933634,0.00008079642,0.00005045393,0.00002368934],"genre_scores_gemma":[0.7009951,0.0001877637,0.2983071,0.00008771038,0.0002583747,0.0000342541,0.00001032922,0.00003123697,0.00008808728],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6393502,"threshold_uncertainty_score":0.9998012,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03704400644000862,"score_gpt":0.3150561453789901,"score_spread":0.2780121389389815,"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."}}