{"id":"W2626336700","doi":"10.1007/s11356-017-9318-5","title":"Synergetic integration of laccase and versatile peroxidase with magnetic silica microspheres towards remediation of biorefinery wastewater","year":2017,"lang":"en","type":"article","venue":"Environmental Science and Pollution Research","topic":"Enzyme-mediated dye degradation","field":"Agricultural and Biological Sciences","cited_by":50,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Sherbrooke","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada; SRM Institute of Science and Technology","keywords":"Laccase; Chemistry; Pentachlorophenol; Immobilized enzyme; Biocatalysis; Chlorophenol; Phenol; Chromatography; Wastewater; Lignin peroxidase; Biorefinery; Phenols; 2,4-Dichlorophenol; Adsorption; Horseradish peroxidase; Nuclear chemistry; Organic chemistry; Catalysis; Enzyme; Waste management","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0008124501,0.00006728712,0.00008371464,0.00003564932,0.0004640346,0.00007133577,0.0001745042,0.00004433579,0.0001636107],"category_scores_gemma":[0.0001411978,0.00002808381,0.00001161653,0.000162408,0.00188063,0.0003660566,0.00012835,0.0000812783,0.000003836165],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004248357,"about_ca_system_score_gemma":0.00001935502,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009017272,"about_ca_topic_score_gemma":0.0002921084,"domain_scores_codex":[0.9988062,0.00005991571,0.0001333338,0.0002247567,0.0005904887,0.0001852946],"domain_scores_gemma":[0.9996513,0.00003875894,0.00009114149,0.00009240807,0.00003874475,0.00008770445],"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":[0.0000391488,0.00003827268,0.01008396,0.000004946972,0.00000105326,6.890726e-7,0.000156019,9.845372e-7,0.942075,0.00003915682,0.00003119477,0.04752957],"study_design_scores_gemma":[0.0001400899,0.0004664574,0.5764389,0.00001912222,0.000003404794,0.00000229634,0.0007680479,0.0002271807,0.4217138,0.00003493991,0.0001370017,0.00004875718],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9986247,0.0001515479,7.186057e-7,0.0008323511,0.00001915396,0.0001593042,0.00004014114,0.000003907149,0.0001681408],"genre_scores_gemma":[0.9994544,0.0002391211,0.0001193266,0.00001059816,0.00002264777,0.000007201708,0.00002010097,6.715041e-7,0.0001259249],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.566355,"threshold_uncertainty_score":0.6929259,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02784848330913409,"score_gpt":0.2667534242334599,"score_spread":0.2389049409243258,"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."}}