{"id":"W4396242623","doi":"10.1016/j.jpowsour.2024.234589","title":"Multiparameter optimization of microbial fuel cell outputs using linear sweep voltammetry and microfluidics","year":2024,"lang":"en","type":"article","venue":"Journal of Power Sources","topic":"Microbial Fuel Cells and Bioremediation","field":"Environmental Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Linear sweep voltammetry; Microbial fuel cell; Microfluidics; Fuel cells; Voltammetry; Cyclic voltammetry; Biological system; Materials science; Chemistry; Computer science; Biochemical engineering; Analytical Chemistry (journal); Process engineering; Nanotechnology; Chemical engineering; Chromatography; Engineering; Electrochemistry; Electrode; Biology","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":[0.0003194868,0.0001073227,0.0001658493,0.0001009536,0.00003672298,0.0000549391,0.0001101164,0.00008817213,0.0003168148],"category_scores_gemma":[0.00002494655,0.0000838493,0.00009400171,0.0001719803,0.0001057533,0.0002226921,0.00007326068,0.0001437584,0.00001189798],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005178239,"about_ca_system_score_gemma":0.00001695885,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006194122,"about_ca_topic_score_gemma":0.00000264083,"domain_scores_codex":[0.9991193,0.00003719261,0.0003883093,0.0001309996,0.0001943709,0.0001298691],"domain_scores_gemma":[0.9995601,0.00004406559,0.0002305406,0.00006639832,0.00002940358,0.00006954533],"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":[0.00003197739,0.00006061823,0.007125054,0.0001033953,0.00001973493,0.0000118035,0.001544696,0.01057551,0.978734,6.798912e-7,0.001127963,0.0006645735],"study_design_scores_gemma":[0.001256837,0.000600924,0.003864551,0.00031195,0.000275461,0.0002399378,0.0005730644,0.06265839,0.8678308,0.0000416717,0.06182391,0.0005224748],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.978459,0.003701119,0.01700348,0.00008075776,0.0005256874,0.00006328454,0.000009027192,0.000006048093,0.0001515801],"genre_scores_gemma":[0.9756688,0.0005581187,0.02342659,0.00008279831,0.0001418228,8.361114e-8,0.000001734845,0.00001455298,0.0001054528],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1109032,"threshold_uncertainty_score":0.3468899,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00871683131358045,"score_gpt":0.2174915176361697,"score_spread":0.2087746863225892,"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."}}