{"id":"W2990497974","doi":"10.11575/prism/33120","title":"Microbial Fuel Cell Application for Carbonaceous and Enhanced Biological Nutrient Remediation with Cathodic Nitrate Reduction","year":2018,"lang":"en","type":"dissertation","venue":"PRISM (University of Calgary)","topic":"Microbial Fuel Cells and Bioremediation","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Environmental remediation; Microbial fuel cell; Cathodic protection; Nitrate; Nutrient; Environmental chemistry; Waste management; Environmental science; Bioremediation; Reduction (mathematics); Chemistry; Environmental engineering; Pulp and paper industry; Ecology; Contamination; Biology; Engineering; Organic chemistry; Electrochemistry; Mathematics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001876948,0.0004034384,0.0003350756,0.0002787301,0.0002862686,0.0005764368,0.000496901,0.000791415,0.0009910072],"category_scores_gemma":[0.0001567683,0.000151096,0.000405475,0.0003012455,0.0001535624,0.0003083839,0.0002792538,0.0004751273,0.0003671217],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009261955,"about_ca_system_score_gemma":0.0004261605,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004101277,"about_ca_topic_score_gemma":0.01111913,"domain_scores_codex":[0.99983,0.0000109657,0.000008261358,0.00004039092,0.00008385531,0.00002645352],"domain_scores_gemma":[0.9999419,0.00000884123,0.000005181599,0.000003832974,0.00003118203,0.000008999647],"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":[0.0000369862,0.0000333009,0.0002446181,0.00008082336,0.000004811475,0.00006091664,0.00001080725,0.0002091811,0.9939777,0.0001085305,0.0001187927,0.005113523],"study_design_scores_gemma":[0.000009685228,0.000325403,0.001919623,0.000009901583,0.00001286078,0.000116586,0.00002972877,0.00175871,0.9908864,0.0000602964,0.00486276,0.000007981362],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.956315,0.006961029,0.02541627,0.0009318427,0.0002597823,0.000177533,0.0005530059,0.0003178924,0.009067744],"genre_scores_gemma":[0.9682971,0.003115406,0.02077941,0.0001919207,0.00002074468,0.0000722047,0.0004942588,0.00001902327,0.00700978],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004101277,"threshold_uncertainty_score":0.008154809,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005029757994847899,"score_gpt":0.1750944668656002,"score_spread":0.1700647088707523,"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."}}