{"id":"W3135543852","doi":"10.1016/j.watres.2021.117038","title":"An evolved native microalgal consortium-snow system for the bioremediation of biogas and centrate wastewater: Start-up, optimization and stabilization","year":2021,"lang":"en","type":"article","venue":"Water Research","topic":"Algal biology and biofuel production","field":"Energy","cited_by":51,"is_retracted":false,"has_abstract":false,"ca_institutions":"Queen's University","funders":"Fundamental Research Funds for the Central Universities; China Association for Science and Technology; Natural Science Foundation of Jiangsu Province; Government of Jiangsu Province; China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Environmental science; Biogas; Wastewater; Sewage treatment; Hydraulic retention time; Photobioreactor; Anaerobic digestion; Biomass (ecology); Pulp and paper industry; Bioreactor; Sequencing batch reactor; Environmental engineering; Waste management; Biofuel; Chemistry; Agronomy; Ecology; Methane; Engineering; Biology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002811752,0.0004125084,0.0003807436,0.0001697116,0.0002605091,0.0006352663,0.0003039006,0.0003230657,0.0003736937],"category_scores_gemma":[0.0002272095,0.000133544,0.0003445014,0.0002715093,0.0001542536,0.0002788545,0.0005899432,0.0003449691,0.0001837345],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003550908,"about_ca_system_score_gemma":0.000547184,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001926744,"about_ca_topic_score_gemma":0.003731345,"domain_scores_codex":[0.999821,0.00003540515,0.00001817119,0.00004090468,0.00005179785,0.00003280249],"domain_scores_gemma":[0.9998959,0.00001124857,0.00001455377,0.00001078516,0.00002010301,0.00004732005],"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":[0.0001391075,0.0001130443,0.0007958215,0.00004461396,0.00001844274,0.0000532051,0.00002196117,0.0006607018,0.9944669,0.00007626782,0.00004310965,0.003566738],"study_design_scores_gemma":[0.00003633446,0.0005274118,0.002728138,0.000005219739,0.00003333035,0.000157339,0.00006429343,0.006029163,0.9888266,0.00005530186,0.00152168,0.00001513383],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9964264,0.0001658062,0.002829595,0.00004133009,0.00001823502,0.00002364324,0.000164725,0.00006234822,0.0002678258],"genre_scores_gemma":[0.9924044,0.0002559016,0.005848126,0.00001829545,0.00000446119,0.00002029855,0.0004228217,0.00002155167,0.001004136],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001926744,"threshold_uncertainty_score":0.003831029,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04664236326870497,"score_gpt":0.3035484510728954,"score_spread":0.2569060878041904,"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."}}