{"id":"W2994231404","doi":"10.30865/komik.v3i1.1675","title":"PEMANFAATAN DATAMINING PADA PENGELOMPOKAN PROVINSI TERHADAP PENCEMARAN LINGKUNGAN HIDUP","year":2019,"lang":"en","type":"article","venue":"KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer)","topic":"Data Mining and Machine Learning Applications","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Innovation Cluster (Canada)","funders":"","keywords":"Pollution; Geography; Java; Environmental pollution; Environmental protection; Ecology; Computer science; Biology","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":[0.00087447,0.0007103074,0.0007457711,0.0004888406,0.0005811317,0.0008741369,0.003832221,0.0003359507,0.0000246453],"category_scores_gemma":[0.0002568679,0.0006270317,0.0002242852,0.0007522346,0.0001865976,0.002475395,0.002723821,0.001202918,0.000647931],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001384049,"about_ca_system_score_gemma":0.0005964329,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003703084,"about_ca_topic_score_gemma":0.00002005441,"domain_scores_codex":[0.9953558,0.0001711498,0.001045956,0.001401483,0.0009077148,0.001117872],"domain_scores_gemma":[0.9955796,0.0005214459,0.0005922972,0.002600331,0.0003133836,0.0003929234],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001137699,0.0006291004,0.01987656,0.0002945104,0.0002824455,0.0002155887,0.0041934,0.001548409,0.007355724,0.5713111,0.01551217,0.3786673],"study_design_scores_gemma":[0.003683808,0.001639373,0.07883927,0.0005443824,0.00006938555,0.001503138,0.001063162,0.2702484,0.003904642,0.001657788,0.6339664,0.002880332],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7523385,0.0002442571,0.2085061,0.006171017,0.003370131,0.002226891,0.00007943786,0.003964552,0.02309904],"genre_scores_gemma":[0.8805441,0.00005222036,0.1152401,0.001138201,0.0002768761,0.00007544584,0.0008835842,0.00005419813,0.001735264],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6184542,"threshold_uncertainty_score":0.9996181,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01530618588813311,"score_gpt":0.2430882416775816,"score_spread":0.2277820557894485,"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."}}