{"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":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.000293542,0.0002391132,0.0002067674,0.00007554607,0.001422394,0.00007359764,0.00009454445,0.0002255267,0.00002406049],"category_scores_gemma":[0.00007354716,0.0001985127,0.00003415875,0.00005433631,0.0004948124,0.0005191617,0.0001034561,0.0002281933,0.000008965491],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001289764,"about_ca_system_score_gemma":0.00001175757,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007559307,"about_ca_topic_score_gemma":0.00006982758,"domain_scores_codex":[0.9986668,0.00007776373,0.0002725727,0.0005927351,0.0001500843,0.0002400287],"domain_scores_gemma":[0.999365,0.000004946232,0.0002761811,0.0002646004,0.00001364498,0.00007557822],"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":[0.005125327,0.0004216621,0.006253195,0.000778429,0.0001874767,0.00003228182,0.001222141,0.001217123,0.8999841,0.0003532957,0.00001045713,0.08441449],"study_design_scores_gemma":[0.002087271,0.0007509332,0.007833363,0.0004411725,0.0001980773,0.002186581,0.002230177,0.0231617,0.9427702,0.005470866,0.01173416,0.00113546],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9945818,0.0004179123,0.002602637,0.0006753483,0.000527214,0.0009212207,0.00003952104,0.00008172749,0.0001526092],"genre_scores_gemma":[0.9988325,0.0004537772,0.0001226437,0.00001680842,0.0002836006,0.00006358853,0.00007487468,0.0000192871,0.000132921],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08327904,"threshold_uncertainty_score":0.9998776,"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."}}