{"id":"W3097693763","doi":"10.1002/awwa.1616","title":"Magnetic Powdered Activated Carbon: A Promising Adsorbent for Water Treatment?","year":2020,"lang":"en","type":"article","venue":"American Water Works Association","topic":"Adsorption and biosorption for pollutant removal","field":"Environmental Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Adsorption; Activated carbon; Water treatment; Powdered activated carbon treatment; Magnetic separation; Reusability; Environmental science; Portable water purification; Waste management; Environmental engineering; Chemistry; Chemical engineering; Process engineering; Environmental chemistry; Materials science; Computer science; Metallurgy; Engineering; Organic chemistry","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":[0.0001066685,0.0001871578,0.0002267359,0.00002581732,0.0001165682,0.00007506941,0.0000986874,0.00007492298,0.0008668309],"category_scores_gemma":[0.00002042932,0.0001171183,0.0001188953,0.0001729769,0.00006149492,0.0001833048,0.00005350025,0.00007956274,0.0003228571],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007625366,"about_ca_system_score_gemma":0.000005689749,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002651091,"about_ca_topic_score_gemma":0.00002474212,"domain_scores_codex":[0.9986168,0.00007271852,0.0002353021,0.0003525653,0.0002592925,0.0004633401],"domain_scores_gemma":[0.9995877,0.00002259233,0.0001252096,0.000107917,0.00001425961,0.000142294],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000168191,0.00009171628,0.008906121,0.000002988539,0.00004960274,0.000002009149,0.00226837,0.0002914219,0.9631677,0.000002158595,0.0005515714,0.02449817],"study_design_scores_gemma":[0.002889363,0.001668162,0.007772069,0.00001652601,0.0001528472,0.000004487173,0.0007192357,0.01116428,0.9133087,0.0001333551,0.06144248,0.0007284674],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9865775,0.000005609236,0.000171451,0.01169368,0.00008504498,0.0005150863,0.00001198534,0.0001323798,0.0008072968],"genre_scores_gemma":[0.9959361,0.00001380826,0.0003623441,0.001232055,0.0001237174,0.00004328772,0.00009172115,0.00002622262,0.002170749],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06089091,"threshold_uncertainty_score":0.949119,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01034603771045895,"score_gpt":0.2135656254532904,"score_spread":0.2032195877428315,"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."}}