{"id":"W4391092779","doi":"10.1109/jestie.2024.3356974","title":"Edge of Transfer Learning-Based Long Short-Term Memory Neural Networks in the Application of Battery Surface Temperature Prediction for Electric Vehicles","year":2024,"lang":"en","type":"article","venue":"IEEE Journal of Emerging and Selected Topics in Industrial Electronics","topic":"Advanced Battery Technologies Research","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Long short term memory; Transfer of learning; Enhanced Data Rates for GSM Evolution; Artificial neural network; Battery (electricity); Term (time); Artificial intelligence; Computer science; Recurrent neural network; Physics; Thermodynamics","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":[0.0004757999,0.0005088256,0.0002907975,0.0002546959,0.0001813086,0.0004315792,0.0006352996,0.0007085365,0.001262984],"category_scores_gemma":[0.001229131,0.0001550393,0.0003144967,0.0003788103,0.000266561,0.0009795335,0.0004037181,0.0007928291,0.0003784704],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004353168,"about_ca_system_score_gemma":0.0004866289,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004132918,"about_ca_topic_score_gemma":0.002939217,"domain_scores_codex":[0.9998577,0.000033319,0.0000107084,0.00003563011,0.00004441269,0.00001818283],"domain_scores_gemma":[0.9997607,0.000105431,0.00002235681,0.00002149549,0.00008098902,0.000008964936],"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":[0.0002657372,0.0001594646,0.002252855,0.0001519386,0.00009479556,0.000203087,0.00008937191,0.5416227,0.01700706,0.004957056,0.003153357,0.4300425],"study_design_scores_gemma":[0.000002052702,0.00002427118,0.0002158175,0.000005654011,0.000006836613,0.00001076456,0.000005509201,0.9955752,0.002550742,0.00114702,0.000452711,0.000003420816],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1797722,0.009745424,0.7928103,0.001803839,0.0005017892,0.00006488437,0.0001545909,0.002302009,0.01284503],"genre_scores_gemma":[0.9449096,0.001783514,0.04872555,0.0002544911,0.0001061003,0.00004528483,0.0001741787,0.00005082444,0.00395046],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004132918,"threshold_uncertainty_score":0.008217752,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.018594169272797,"score_gpt":0.2687943498114155,"score_spread":0.2502001805386185,"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."}}