{"id":"W3023528254","doi":"10.5004/dwt.2020.25096","title":"Adsorptive removal of nickel by modified natural adsorbents: optimization, characterization and application","year":2020,"lang":"en","type":"article","venue":"Desalination and Water Treatment","topic":"Extraction and Separation Processes","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ministry of Agriculture","funders":"","keywords":"Nickel; Adsorption; Characterization (materials science); Natural (archaeology); Materials science; Chemistry; Chemical engineering; Metallurgy; Nanotechnology; Engineering; Organic chemistry; Geology","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":[0.00002333941,0.0001029627,0.0001072188,0.00003955565,0.00004130552,0.00002723005,0.00002087018,0.00004428927,0.00001252494],"category_scores_gemma":[0.000004793426,0.00008081652,0.00001401157,0.00006746752,0.000019027,0.0003634139,0.000005604769,0.00002802171,0.000003993484],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002747816,"about_ca_system_score_gemma":0.000003963948,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003977259,"about_ca_topic_score_gemma":7.610726e-7,"domain_scores_codex":[0.9995252,0.00001459504,0.0001804374,0.0001372448,0.00007432055,0.00006813448],"domain_scores_gemma":[0.9997852,0.000008549132,0.00003762949,0.00004444147,0.00007231673,0.00005183206],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001312229,0.0001775415,0.0003422833,0.0001938867,0.0001646484,0.000002359593,0.005897836,0.05478574,0.8860143,0.002891106,0.0002177367,0.0491813],"study_design_scores_gemma":[0.0009978137,0.00008895522,0.0004057354,0.000006738014,0.00003339054,0.000008800385,0.00008381403,0.7656435,0.2226589,0.0000362161,0.009891291,0.0001447392],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4822059,0.0004229487,0.5149797,0.001278626,0.00007419025,0.0003873314,0.00005358345,0.0001850328,0.0004127084],"genre_scores_gemma":[0.997142,0.0006014738,0.0006537785,0.0001323499,0.00002092436,0.00003594398,0.001038835,0.00001269987,0.0003620416],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7108578,"threshold_uncertainty_score":0.3295602,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009183971297354902,"score_gpt":0.2172827688142998,"score_spread":0.2080987975169449,"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."}}