{"id":"W1712834714","doi":"10.29122/jai.v6i1.2447","title":"METODA PENGHILANGAN LOGAM MERKURI DI DALAM AIR LIMBAH INDUSTRI","year":2018,"lang":"en","type":"article","venue":"Jurnal Air Indonesia","topic":"Engineering and Technology Innovations","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Discovery Air (Canada)","funders":"","keywords":"Mercury (programming language); Wastewater; Hazardous waste; Industrial wastewater treatment; Environmental chemistry; Pollutant; Environmental science; Reverse osmosis; Cadmium; Chemistry; Adsorption; Pollution; Waste management; Environmental engineering","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":[0.00014665,0.0004783424,0.000295599,0.0006190877,0.001127844,0.001529931,0.0002951712,0.0004362933,0.02284409],"category_scores_gemma":[0.000160214,0.0002040151,0.0002033607,0.0006856887,0.0004222564,0.0006683249,0.0009333608,0.0007704804,0.003560534],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007938932,"about_ca_system_score_gemma":0.001629725,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002447074,"about_ca_topic_score_gemma":0.006832981,"domain_scores_codex":[0.9998671,0.00001163805,0.00001032443,0.00003212616,0.00004783927,0.00003095624],"domain_scores_gemma":[0.9999211,0.00001068025,0.00001420791,0.00000649759,0.00002855114,0.0000189308],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0006291046,0.0005600512,0.03122945,0.002372751,0.00005986461,0.0038002,0.002519626,0.001082066,0.08823808,0.0134981,0.02322633,0.8327843],"study_design_scores_gemma":[0.00004597099,0.0004234176,0.07289664,0.0003467079,0.00007457147,0.004072703,0.003529151,0.001562939,0.02952334,0.002691791,0.8847699,0.0000628171],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5482402,0.03695377,0.01036774,0.006955105,0.001864978,0.000324283,0.001350921,0.001145307,0.3927976],"genre_scores_gemma":[0.698161,0.01588636,0.01079199,0.0008014578,0.0001799701,0.0001036641,0.0006790432,0.0001335455,0.2732631],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02284409,"threshold_uncertainty_score":0.07642114,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01018728798590031,"score_gpt":0.2161003793749237,"score_spread":0.2059130913890233,"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."}}