{"id":"W4403947257","doi":"10.3390/nano14211747","title":"Heterogeneous Catalytic Ozonation of Pharmaceuticals: Optimization of the Process by Response Surface Methodology","year":2024,"lang":"en","type":"article","venue":"Nanomaterials","topic":"Advanced oxidation water treatment","field":"Environmental Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Generalitat de Catalunya; European Commission; Sveučilište u Zagrebu; Centres de Recerca de Catalunya; Canadian Institute for Advanced Research","keywords":"Response surface methodology; Process (computing); Catalysis; Process engineering; Chemistry; Process optimization; Environmental science; Biochemical engineering; Chemical engineering; Environmental chemistry; Computer science; Organic chemistry; Environmental engineering; Chromatography; Engineering","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001370711,0.001127653,0.0009317873,0.0003962455,0.0002596591,0.0006297727,0.0006731109,0.0008686557,0.000527103],"category_scores_gemma":[0.0005941822,0.000347059,0.0007876153,0.0005225544,0.0003423951,0.0004139563,0.0004390767,0.0007297508,0.0003569358],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005051842,"about_ca_system_score_gemma":0.0005121012,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001260401,"about_ca_topic_score_gemma":0.002369737,"domain_scores_codex":[0.9990721,0.0001654192,0.00009218947,0.0002164135,0.0003195993,0.0001343373],"domain_scores_gemma":[0.9998275,0.00005415921,0.00003449098,0.00001549386,0.00005483724,0.00001352054],"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":[0.00008028862,0.00008656814,0.00008415779,0.0001192733,0.00001087512,0.00002417422,0.00001428312,0.0006672831,0.9960476,0.00005059197,0.0000232336,0.002791594],"study_design_scores_gemma":[0.00001670838,0.0003559929,0.0008157833,0.00000475087,0.00002367975,0.00003747505,0.00001574062,0.003025494,0.9947764,0.00002800751,0.0008883554,0.0000115482],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8979948,0.003723988,0.09418231,0.0001945081,0.00008568447,0.0008117713,0.0004254631,0.0003018401,0.00227976],"genre_scores_gemma":[0.8817211,0.003805926,0.1115216,0.00008074352,0.00002723287,0.0006453143,0.000586309,0.00008129815,0.001530628],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001370711,"threshold_uncertainty_score":0.007249117,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03445517221233643,"score_gpt":0.3417700835699826,"score_spread":0.3073149113576462,"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."}}