{"id":"W7119097371","doi":"10.58169/jwikal.v4i2.993","title":"Analisis Kinerja IPAL PT X Jawa Timur dalam Menurunkan Parameter Pencemar Menggunakan Pendekatan Water Quality Index (WQI)","year":2025,"lang":"","type":"article","venue":"JURNAL WILAYAH KOTA DAN LINGKUNGAN BERKELANJUTAN","topic":"Marine and Coastal Ecosystems","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Effluent; Wastewater; Water quality; Sewage treatment; Index (typography); Inlet","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.00119504,0.0007041607,0.0008582571,0.003265135,0.0005603648,0.002446644,0.0005036682,0.0005915423,0.005494949],"category_scores_gemma":[0.001521329,0.000305746,0.0007185555,0.003769928,0.0005136403,0.001051182,0.0008040177,0.0007163491,0.002158041],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005020452,"about_ca_system_score_gemma":0.000677405,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005655928,"about_ca_topic_score_gemma":0.009401565,"domain_scores_codex":[0.9985545,0.0001094576,0.000146387,0.0002363543,0.000858614,0.00009466682],"domain_scores_gemma":[0.9992185,0.0001899626,0.0001629114,0.00004028184,0.0003559754,0.00003243832],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00141517,0.0003897111,0.2677013,0.00226534,0.0004272724,0.00134775,0.003747048,0.005612617,0.2426164,0.00193028,0.006130861,0.4664162],"study_design_scores_gemma":[0.00002507284,0.0006556858,0.7730718,0.0002032996,0.0003327657,0.00146334,0.006885337,0.02304847,0.1504505,0.001265482,0.04237489,0.0002232758],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9391083,0.001919135,0.02741838,0.0002854997,0.0001078728,0.0002299427,0.005840402,0.001066671,0.02402383],"genre_scores_gemma":[0.9455702,0.001125562,0.03644234,0.0001135537,0.00002131794,0.0002387582,0.003229003,0.0002376236,0.01302167],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005655928,"threshold_uncertainty_score":0.01838243,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01178532785139555,"score_gpt":0.2676100400372413,"score_spread":0.2558247121858457,"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."}}