{"id":"W4411834680","doi":"10.62177/jaet.v2i3.478","title":"Explainable AI for Battery Degradation Prediction in EVs: Toward Transparent Energy Forecasting","year":2025,"lang":"en","type":"article","venue":"Journal of advances in engineering and technology.","topic":"Electric Vehicles and Infrastructure","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Battery (electricity); Degradation (telecommunications); Computer science; Energy (signal processing); Reliability engineering; Automotive engineering; Environmental science; Engineering; Telecommunications; Statistics; Mathematics; Physics; Power (physics)","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":[0.001034718,0.0006844127,0.0004735132,0.0005679798,0.0002315504,0.0009024879,0.0009838847,0.0007610766,0.001147954],"category_scores_gemma":[0.004330665,0.000247058,0.000622009,0.0004082326,0.0005954465,0.001444567,0.001003306,0.001656423,0.0001555072],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005854914,"about_ca_system_score_gemma":0.0006130604,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003198514,"about_ca_topic_score_gemma":0.003836265,"domain_scores_codex":[0.9997202,0.000111496,0.00001668267,0.00007154829,0.00005379364,0.00002635654],"domain_scores_gemma":[0.9984064,0.001038772,0.0001992681,0.0001497031,0.0001552498,0.00005069863],"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":[0.00008097286,0.00007028217,0.005470612,0.000118478,0.0001379665,0.000180143,0.0002154607,0.8755806,0.001991632,0.0427551,0.001683497,0.07171512],"study_design_scores_gemma":[0.000002565683,0.00001163795,0.000250913,0.00000823044,0.000009135341,0.00001144011,0.00001044665,0.9810916,0.0002304735,0.01806791,0.0003013898,0.000004279689],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05960871,0.0007288225,0.9347801,0.001529442,0.00007237899,0.00003793533,0.000283763,0.0006707719,0.002288166],"genre_scores_gemma":[0.9388669,0.0003874329,0.05899193,0.000214692,0.00008925555,0.00005163287,0.0003031916,0.00004467992,0.001050359],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003198514,"threshold_uncertainty_score":0.006359756,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005331069096937814,"score_gpt":0.2064241516538981,"score_spread":0.2010930825569603,"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."}}