{"id":"W2730942687","doi":"10.1016/j.psep.2017.06.013","title":"Integrated Haematococcus pluvialis biomass production and nutrient removal using bioethanol plant waste effluent","year":2017,"lang":"en","type":"article","venue":"Process Safety and Environmental Protection","topic":"Algal biology and biofuel production","field":"Energy","cited_by":61,"is_retracted":false,"has_abstract":false,"ca_institutions":"Ministry of Agriculture, Food and Rural Affairs; University of Guelph","funders":"Ontario Ministry of Agriculture, Food and Rural Affairs","keywords":"Haematococcus pluvialis; Photobioreactor; Wastewater; Bioenergy; Biomass (ecology); Pulp and paper industry; Biofuel; Effluent; Environmental science; Sewage treatment; Pluvialis; Astaxanthin; Bioreactor; Waste management; Environmental engineering; Chemistry; Food science; Agronomy; Botany; Biology; Engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001287554,0.0005315497,0.0002632687,0.000264535,0.0002085702,0.0005839941,0.0002493304,0.0002365725,0.000731814],"category_scores_gemma":[0.00009736493,0.0001302865,0.0003060788,0.000294983,0.0001478368,0.00024419,0.0005217852,0.0004077707,0.0002851858],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003136159,"about_ca_system_score_gemma":0.0003923358,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001173892,"about_ca_topic_score_gemma":0.002464856,"domain_scores_codex":[0.9998108,0.00001481782,0.00001293406,0.00003388584,0.00008126255,0.00004625346],"domain_scores_gemma":[0.9999598,0.000004470379,0.000007042881,0.000006900287,0.00001080525,0.00001090665],"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":[0.00006998574,0.00009724752,0.0004134204,0.0000239461,0.000004859789,0.00003121749,0.000008750683,0.0002426834,0.9951697,0.00004024727,0.00001422975,0.003883687],"study_design_scores_gemma":[0.000009020436,0.0001698291,0.001985735,0.000002672189,0.0000096796,0.00003451205,0.00002282182,0.0008184364,0.9965407,0.00002230802,0.0003823068,0.000001987795],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9962388,0.0001327261,0.002346018,0.00003053333,0.00001269021,0.00001827124,0.00009971092,0.00004154794,0.001079708],"genre_scores_gemma":[0.9935736,0.0001331418,0.003287025,0.00002110926,0.000003670884,0.00001520958,0.0002174118,0.00001160586,0.002737175],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001173892,"threshold_uncertainty_score":0.002448201,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02306078065641861,"score_gpt":0.2336015062187408,"score_spread":0.2105407255623222,"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."}}