{"id":"W4396214396","doi":"10.1109/access.2024.3394843","title":"Comparing Hybrid Approaches of Deep Learning for Remaining Useful Life Prognostic of Lithium-Ion Batteries","year":2024,"lang":"en","type":"article","venue":"IEEE Access","topic":"Advanced Battery Technologies Research","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Lithium (medication); Computer science; Ion; Artificial intelligence; Physics; Medicine; Internal medicine","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":[0.001239918,0.00151943,0.0007726299,0.001207859,0.0002734277,0.00126586,0.00136248,0.0009623382,0.001334352],"category_scores_gemma":[0.002602631,0.0003021784,0.0008475677,0.0008203983,0.0003276386,0.001923692,0.0009122312,0.0008733972,0.0003820414],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008561054,"about_ca_system_score_gemma":0.0007386728,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008032994,"about_ca_topic_score_gemma":0.00652773,"domain_scores_codex":[0.9996004,0.0001005257,0.00004218783,0.00009402668,0.0001116971,0.00005130967],"domain_scores_gemma":[0.9992527,0.000397744,0.00004716697,0.00004931953,0.0002182674,0.00003473154],"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":[0.00042555,0.0001346365,0.009419061,0.0004360182,0.0003076864,0.0001132316,0.00008805376,0.8061612,0.001620059,0.003152359,0.002401265,0.1757409],"study_design_scores_gemma":[0.000008942347,0.0001037355,0.0007522102,0.00003317784,0.00003924653,0.00001984971,0.00003379077,0.9956509,0.001115476,0.001649256,0.0005799975,0.00001337435],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3579985,0.01747724,0.6001246,0.002028715,0.0005033112,0.0001940299,0.001543532,0.00270857,0.01742147],"genre_scores_gemma":[0.9662976,0.002354604,0.02718632,0.0002241683,0.00007956523,0.000106098,0.0007982193,0.00005552457,0.002897894],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008032994,"threshold_uncertainty_score":0.0159725,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0991895451366036,"score_gpt":0.3137589795740182,"score_spread":0.2145694344374146,"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."}}