{"id":"W4417162191","doi":"10.1016/j.jpowsour.2025.239050","title":"State-of-health forecasting of solid oxide fuel cells using physics-informed temporal graph convolutional network","year":2025,"lang":"en","type":"article","venue":"Journal of Power Sources","topic":"Advancements in Solid Oxide Fuel Cells","field":"Materials Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Innovates; Fusion Energy Sciences; University of Alberta; Cummins Incorporated","keywords":"Graph; Fuel cells; Solid oxide fuel cell; Oxide; Convolutional neural network","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002675185,0.0005842667,0.0003005977,0.0003729871,0.0001528537,0.0003880443,0.0007015166,0.0005114836,0.0006714874],"category_scores_gemma":[0.001064578,0.0002311485,0.0004382047,0.000302866,0.0002141447,0.0004878044,0.0003304872,0.0007047551,0.0001394915],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00088048,"about_ca_system_score_gemma":0.0006420864,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02646294,"about_ca_topic_score_gemma":0.02966044,"domain_scores_codex":[0.9999328,0.000008943267,0.000003324962,0.0000264951,0.00001437784,0.00001400833],"domain_scores_gemma":[0.9997967,0.000100101,0.00002757936,0.00001600979,0.00004870431,0.00001094291],"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":[0.00007173529,0.00003952338,0.00249368,0.00002258995,0.00003576311,0.00004777233,0.00001524297,0.9617626,0.002579404,0.0008447459,0.000606655,0.03148033],"study_design_scores_gemma":[5.825977e-7,0.000002643432,0.0001787739,6.499688e-7,0.000002323689,0.000002397933,8.338564e-7,0.9992291,0.0002585041,0.0002869001,0.00003624016,9.69923e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5032866,0.001304578,0.4877603,0.0008555454,0.0001378058,0.00004388336,0.001269383,0.001670484,0.003671334],"genre_scores_gemma":[0.9858404,0.0001960931,0.01214148,0.00005936887,0.00001676055,0.00002140733,0.0005957154,0.0000217308,0.001106884],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02646294,"threshold_uncertainty_score":0.05261785,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03273457692696664,"score_gpt":0.31069374914231,"score_spread":0.2779591722153433,"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."}}