{"id":"W2889100438","doi":"10.1016/j.biortech.2018.08.108","title":"Optimizing the configuration of integrated nutrient and energy recovery treatment trains: A new application of global sensitivity analysis to the generic nutrient recovery model (NRM) library","year":2018,"lang":"en","type":"article","venue":"Bioresource Technology","topic":"Phosphorus and nutrient management","field":"Environmental Science","cited_by":12,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université Laval","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada","keywords":"Environmental science; Resource recovery; Bioenergy; Process engineering; Production (economics); Energy recovery; Sensitivity (control systems); Biofuel; Computer science; Environmental engineering; Engineering; Waste management; Energy (signal processing); Mathematics; Statistics","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":[0.000171884,0.0001691005,0.0002547851,0.0001206592,0.0001247528,0.0000191037,0.0002476841,0.0001139353,0.00002906366],"category_scores_gemma":[0.0000195094,0.0001037793,0.000098349,0.001978043,0.0004522622,0.00007760871,0.0002634857,0.00004736226,0.000006040725],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002076542,"about_ca_system_score_gemma":0.00002719506,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001321559,"about_ca_topic_score_gemma":0.0004905632,"domain_scores_codex":[0.9988513,0.00006422744,0.0003070808,0.0003884635,0.0001796975,0.0002092455],"domain_scores_gemma":[0.9991011,0.0000338669,0.0002327887,0.0005558304,0.00001684516,0.00005953723],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001174356,0.0009949914,0.01983056,0.00001253884,0.00108805,0.000005073975,0.001799123,0.1049015,0.05571793,0.0312292,0.006606511,0.7766401],"study_design_scores_gemma":[0.001452782,0.002885456,0.01640674,0.00003921282,0.001149024,0.00001306373,0.001996007,0.4862659,0.3161466,0.01148464,0.1615293,0.0006312657],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6368018,0.000239128,0.3597025,0.002324139,0.0000219777,0.000323794,0.00005057363,0.00005335113,0.0004826968],"genre_scores_gemma":[0.9961517,0.0003255408,0.002973967,0.0002771884,0.00002060444,0.00006210767,0.00002389484,0.000008740694,0.0001562648],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7760088,"threshold_uncertainty_score":0.4231997,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008286706379751848,"score_gpt":0.2021397631286969,"score_spread":0.1938530567489451,"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."}}