{"id":"W4401211296","doi":"10.1016/j.ijhydene.2024.07.448","title":"Remaining useful life prognostic-based energy management strategy for multi-fuel cell stack systems in automotive applications","year":2024,"lang":"en","type":"article","venue":"International Journal of Hydrogen Energy","topic":"Advanced Battery Technologies Research","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"Université de Franche-Comté; Agence Nationale de la Recherche; Université du Québec à Trois-Rivières","keywords":"Proton exchange membrane fuel cell; Stack (abstract data type); Automotive industry; Computer science; Automotive engineering; Fuel efficiency; Battery (electricity); Process engineering; Fuel cells; Power (physics); Engineering; Operating system","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":[0.0002111222,0.0001732447,0.0002059887,0.0008048933,0.00002459681,0.0001222888,0.0006250527,0.00009005958,0.00001197475],"category_scores_gemma":[0.00003903264,0.000168225,0.0001108687,0.0002841234,0.00003649861,0.0002250655,0.00006817235,0.0002044514,0.000004533981],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004356103,"about_ca_system_score_gemma":0.0001010149,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001394691,"about_ca_topic_score_gemma":0.00003107493,"domain_scores_codex":[0.9984396,0.00002762204,0.0006111829,0.0002017407,0.0004403644,0.0002795245],"domain_scores_gemma":[0.9991915,0.0001846364,0.0001076774,0.0001490748,0.0002813705,0.00008573907],"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":[0.00003234128,0.00009502844,0.00007125488,0.0001823083,0.0003181098,0.0002410298,0.00003697992,0.9770244,0.001476231,0.007341271,0.0002336478,0.01294733],"study_design_scores_gemma":[0.0008065189,0.00008378091,0.00004206053,0.0002590023,0.00002241465,0.0000341034,0.0002510608,0.9203205,0.009744329,0.000935422,0.0673063,0.0001944815],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007280556,0.005163308,0.9855899,0.0001469856,0.0004947378,0.0002011507,0.00006031503,0.0002139618,0.0008490451],"genre_scores_gemma":[0.9890033,0.0004837179,0.009811546,0.00003655038,0.0001659368,0.0002337286,0.0000300537,0.00005758749,0.0001776047],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9817227,"threshold_uncertainty_score":0.6860017,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03296834619438734,"score_gpt":0.3030052461943841,"score_spread":0.2700368999999967,"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."}}