{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001454907,0.000249748,0.0002431193,0.0003680556,0.0001491541,0.0000191857,0.0003354596,0.0003517422,0.00002051677],"category_scores_gemma":[0.00003515739,0.0002458233,0.00006963918,0.0008514299,0.0001181108,0.0001595066,0.00005461549,0.0007599077,0.000186657],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008540449,"about_ca_system_score_gemma":0.00002996341,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007356034,"about_ca_topic_score_gemma":0.00001198349,"domain_scores_codex":[0.9988552,0.00001383924,0.0003188682,0.0002056098,0.0001860159,0.00042044],"domain_scores_gemma":[0.9993239,0.00004067965,0.00004770424,0.0003836826,0.0001094221,0.00009459391],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0001222876,0.001015436,0.2320829,0.0006087966,0.003006224,0.0008120057,0.002898883,0.06006577,0.3216912,0.137517,0.1294295,0.1107501],"study_design_scores_gemma":[0.001939756,0.0005306114,0.5630537,0.0002332692,0.0001644084,0.0005698806,0.0002400251,0.01378229,0.2691943,0.0008830689,0.1477406,0.00166802],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9863368,0.0001380136,0.005344631,0.0009463658,0.0008986603,0.0001318377,0.00001011922,0.001709714,0.00448387],"genre_scores_gemma":[0.9982808,0.00001833033,0.000613616,0.0001130615,0.0006276655,0.00003087037,0.00001653922,0.0000636194,0.0002355513],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3309709,"threshold_uncertainty_score":0.9999994,"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."}}