{"id":"W4417411625","doi":"10.1021/acssuschemeng.5c09770","title":"Benchmarking Machine Learning Algorithms for Microbial Electromethanogenesis: A Comprehensive Assessment with SHapley Additive exPlanation-Based Insights","year":2025,"lang":"en","type":"article","venue":"ACS Sustainable Chemistry & Engineering","topic":"Microbial Fuel Cells and Bioremediation","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada); Centre for Global Health Research","funders":"Departament d'Universitats, Recerca i Societat de la Informació; Agència de Gestió d'Ajuts Universitaris i de Recerca; Ministerio de Ciencia y Tecnología; Natural Environment Research Council; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Institució Catalana de Recerca i Estudis Avançats; Universitat de Girona","keywords":"Hyperparameter; Boosting (machine learning); Multilayer perceptron; Gradient boosting; Artificial neural network; Interpretability; Workflow; Convolutional neural network; Process (computing)","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007055209,0.001326094,0.0009694707,0.001463208,0.0002455279,0.0009525152,0.001119842,0.001125161,0.000542421],"category_scores_gemma":[0.01199958,0.0003110995,0.0008884138,0.000647893,0.0005101196,0.001374055,0.0009923276,0.001129123,0.0001169381],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007956405,"about_ca_system_score_gemma":0.001069316,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002262386,"about_ca_topic_score_gemma":0.002763026,"domain_scores_codex":[0.9980955,0.001061932,0.0001515263,0.0002630255,0.0003468723,0.00008119915],"domain_scores_gemma":[0.9919102,0.006187138,0.000415547,0.0006080655,0.0007681351,0.0001109707],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001789758,0.000164425,0.009635129,0.0002478613,0.0003850969,0.00005170528,0.00005599382,0.9046893,0.00132007,0.002692785,0.0006769436,0.07990172],"study_design_scores_gemma":[0.000006129739,0.0000816391,0.0005881878,0.00001023543,0.0000164612,0.000006619066,0.000009154492,0.9976292,0.0006739573,0.0008616646,0.0001124488,0.000004348609],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6495601,0.004867563,0.3393511,0.001106412,0.0001104781,0.0001558002,0.0004431522,0.001315507,0.003089769],"genre_scores_gemma":[0.9180056,0.0007740484,0.0797336,0.0001577948,0.00003499504,0.00008392738,0.0006958159,0.0000742038,0.0004399341],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007055209,"threshold_uncertainty_score":0.03731197,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004123924229456281,"score_gpt":0.2033906083834148,"score_spread":0.1992666841539585,"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."}}