{"id":"W4387735687","doi":"10.1021/acsestwater.3c00215","title":"Machine Learning for Heavy Metal Removal from Water: Recent Advances and Challenges","year":2023,"lang":"en","type":"article","venue":"ACS ES&T Water","topic":"Water Quality Monitoring Technologies","field":"Environmental Science","cited_by":41,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta; Suncor Energy (Canada)","funders":"Southeast University; National Research Foundation of Korea; Rural Development Administration; Korea University","keywords":"Computer science; Implementation; Biochar; Data science; Engineering; Waste management; Software 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":[],"consensus_categories":[],"category_scores_codex":[0.0004251489,0.0001716495,0.0001910407,0.00004489247,0.000186733,0.00004034039,0.0002520498,0.0000955949,0.0001022541],"category_scores_gemma":[0.00005036549,0.000102436,0.00003589406,0.00005247995,0.000150308,0.0002688225,0.0006901411,0.0001450178,0.0005276438],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004832611,"about_ca_system_score_gemma":9.24507e-7,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001570682,"about_ca_topic_score_gemma":0.00004561262,"domain_scores_codex":[0.9986351,0.00005889449,0.0001836721,0.0004414274,0.0002087753,0.0004721745],"domain_scores_gemma":[0.9996,0.00006298933,0.00002544196,0.0002561582,0.000006414126,0.00004899546],"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":[0.0002678304,0.0001334306,0.02057474,0.0001841694,0.0001465652,0.0001078989,0.01043671,0.001586816,0.3932931,0.0001639952,0.00069615,0.5724086],"study_design_scores_gemma":[0.0002260314,0.00008919846,0.001336598,0.000008354978,0.00001282817,0.000004239258,0.0002180321,0.0001626204,0.4854272,0.007169771,0.5051768,0.0001683374],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.986357,0.002110453,0.00002705226,0.01028328,0.0002980724,0.0001929263,0.00001063921,0.0005551939,0.0001654095],"genre_scores_gemma":[0.9788809,0.01726427,0.002322352,0.0000463707,0.0001117274,0.0000701953,0.0001079948,0.00003727541,0.001158873],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5722403,"threshold_uncertainty_score":0.6781969,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0582817784144826,"score_gpt":0.2717285397397013,"score_spread":0.2134467613252188,"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."}}