{"id":"W4313563309","doi":"10.1109/vppc55846.2022.10003313","title":"Fuel Cell Ageing Prediction and Remaining Useful Life Forecasting","year":2022,"lang":"en","type":"article","venue":"2022 IEEE Vehicle Power and Propulsion Conference (VPPC)","topic":"Fuel Cells and Related Materials","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke; Université du Québec à Trois-Rivières","funders":"","keywords":"Benchmark (surveying); Long short term memory; Computer science; Generalization; Mean squared error; Estimation; Term (time); Reliability engineering; Degradation (telecommunications); Artificial neural network; Machine learning; Artificial intelligence; Engineering; Statistics; Recurrent neural network; Mathematics; Telecommunications","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":[0.0002737867,0.0005850738,0.000369373,0.0007533877,0.000158321,0.0003262018,0.0004017002,0.0005350336,0.000679272],"category_scores_gemma":[0.001066165,0.00009882442,0.000255047,0.0004809643,0.00008299411,0.0005051808,0.0002024716,0.0003791815,0.0003952672],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004474645,"about_ca_system_score_gemma":0.000266565,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01034429,"about_ca_topic_score_gemma":0.007970078,"domain_scores_codex":[0.9999125,0.000008196938,0.000005906499,0.00002932191,0.00002914199,0.00001492689],"domain_scores_gemma":[0.9997112,0.00007378974,0.0000374926,0.00003015058,0.0001345297,0.0000127329],"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":[0.0002163572,0.0001156345,0.02539897,0.0001014169,0.00003785416,0.0002067448,0.00004528589,0.7046723,0.01521818,0.0004202983,0.003694226,0.2498727],"study_design_scores_gemma":[0.000002126272,0.00002196074,0.003657949,0.000005770564,0.000005048106,0.00001945438,0.00001123444,0.9875447,0.007868403,0.0002983338,0.000559513,0.000005551312],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8286291,0.001518281,0.1573489,0.000340884,0.0001607456,0.00005069715,0.003418746,0.002825537,0.005707102],"genre_scores_gemma":[0.9885015,0.0001794632,0.008845292,0.00001847393,0.000015648,0.00001646996,0.001432318,0.00001877538,0.0009719434],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01034429,"threshold_uncertainty_score":0.02056819,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01882331218797027,"score_gpt":0.1907942363713435,"score_spread":0.1719709241833733,"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."}}