{"id":"W3196971317","doi":"10.3390/fermentation7030169","title":"Agricultural Waste and Wastewater as Feedstock for Bioelectricity Generation Using Microbial Fuel Cells: Recent Advances","year":2021,"lang":"en","type":"article","venue":"Fermentation","topic":"Microbial Fuel Cells and Bioremediation","field":"Environmental Science","cited_by":174,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Microbial fuel cell; Waste management; Renewable energy; Commercialization; Environmental science; Fossil fuel; Raw material; Biodegradable waste; Agriculture; Electricity generation; Engineering; Business; Chemistry; Ecology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009242833,0.0001039939,0.00008136153,0.00001758749,0.0001830916,0.00007431245,0.00003625746,0.00005161265,0.00033417],"category_scores_gemma":[0.00001123008,0.0000869168,0.00002955415,0.0001479805,0.00002683526,0.0003191109,0.00003672911,0.00004188627,0.00002898963],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001712855,"about_ca_system_score_gemma":0.00001212252,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004705558,"about_ca_topic_score_gemma":0.0001523068,"domain_scores_codex":[0.9992246,0.00005017295,0.0001708363,0.0002694915,0.0001185943,0.000166279],"domain_scores_gemma":[0.9997756,0.000009350975,0.00008531002,0.00005617322,0.00002954104,0.00004398877],"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":[0.00001919129,0.00003376597,0.0002376473,0.00001956593,0.000003993616,5.128288e-7,0.0002554977,0.0005087318,0.9927144,0.000004277395,0.0009419259,0.005260461],"study_design_scores_gemma":[0.000439955,0.00008057031,0.001157559,0.00000553764,0.00002497709,0.000009907861,0.0002477051,0.001719148,0.9917439,0.00007328808,0.004356497,0.0001409251],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9980359,0.0002188419,0.0005936743,0.0002063038,0.0003870325,0.000349709,0.00001596989,0.00001239268,0.0001801677],"genre_scores_gemma":[0.9916118,0.0009569156,0.005798262,0.000244771,0.0002927544,0.00001609037,0.0004438677,0.000009984023,0.0006255356],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006424088,"threshold_uncertainty_score":0.3658928,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01724357186326146,"score_gpt":0.2386966225356736,"score_spread":0.2214530506724122,"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."}}