{"id":"W4404238552","doi":"10.1109/access.2024.3495560","title":"Transformer-Based Deep Learning Strategies for Lithium-Ion Batteries SOX Estimation Using Regular and Inverted Embedding","year":2024,"lang":"en","type":"article","venue":"IEEE Access","topic":"Advanced Battery Technologies Research","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Computer science; Embedding; Transformer; Deep learning; Ion; Lithium (medication); Materials science; Electrical engineering; Artificial intelligence; Chemistry; Engineering; Voltage","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001277828,0.0001708245,0.0001588929,0.0002790962,0.0001481994,0.0005823462,0.0001887091,0.0001243233,0.00001225154],"category_scores_gemma":[0.00004070433,0.000171203,0.00003728531,0.0003204189,0.00008897992,0.001575264,0.00001850876,0.0002755739,0.000002249722],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001027226,"about_ca_system_score_gemma":0.00003180632,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001075427,"about_ca_topic_score_gemma":0.00001410219,"domain_scores_codex":[0.9990947,0.00001652256,0.0001896638,0.000236422,0.0001465277,0.0003162245],"domain_scores_gemma":[0.9996184,0.0001576346,0.0000174288,0.0001312599,0.0000401748,0.00003515058],"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":[0.00001405764,0.000003248187,0.00007132002,0.001147798,0.00002773413,0.000007468894,0.0002194878,0.7823979,0.143959,0.0001312579,0.00004327603,0.07197755],"study_design_scores_gemma":[0.0001509696,0.00003457239,0.00003379924,0.0001951491,0.00001385414,0.000005436542,0.0002929654,0.841013,0.1545264,0.003110266,0.0004540528,0.0001696308],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3713703,0.0003634771,0.6269678,0.00007903499,0.0001723311,0.0001978936,0.000005310635,0.0007936677,0.00005021368],"genre_scores_gemma":[0.9868029,0.00006028819,0.01289901,0.00001562534,0.00004766136,0.00008092925,0.00002038346,0.00005979045,0.00001339247],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6154327,"threshold_uncertainty_score":0.6981456,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03936813705671466,"score_gpt":0.3440342812918876,"score_spread":0.304666144235173,"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."}}