{"id":"W4401804314","doi":"10.3390/fire7090296","title":"Enhancing Fire Protection in Electric Vehicle Batteries Based on Thermal Energy Storage Systems Using Machine Learning and Feature Engineering","year":2024,"lang":"en","type":"article","venue":"Fire","topic":"Advanced Battery Technologies Research","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Feature (linguistics); Automotive engineering; Electric vehicle; Energy storage; Thermal; Computer science; Thermal energy storage; Environmental science; Engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0001967989,0.0003300114,0.0003427924,0.0002851395,0.0001409211,0.000453917,0.0003579937,0.0002290411,0.000580413],"category_scores_gemma":[0.0005884638,0.0001088517,0.0003313728,0.000241728,0.0001401257,0.0006790866,0.000255624,0.0002087049,0.0001417636],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002547102,"about_ca_system_score_gemma":0.0001839818,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009530004,"about_ca_topic_score_gemma":0.001508072,"domain_scores_codex":[0.9999253,0.00001463049,0.000006895286,0.00001289319,0.00003032925,0.000009975794],"domain_scores_gemma":[0.9998461,0.00006129107,0.00003053868,0.00001345692,0.00004430075,0.000004216391],"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":[0.0002933895,0.0001678518,0.009260878,0.0002788548,0.00007806916,0.0002953925,0.0001202581,0.6458338,0.04980746,0.001940908,0.0006627966,0.2912603],"study_design_scores_gemma":[0.000006233869,0.0001776089,0.001820375,0.00001741855,0.00002451153,0.00007317415,0.00003103013,0.9794418,0.0168886,0.0008311081,0.0006796883,0.000008497594],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.521506,0.00102446,0.4695529,0.0002033604,0.00005094772,0.00005931478,0.0000994218,0.001083895,0.006419748],"genre_scores_gemma":[0.9933449,0.0001524024,0.005947909,0.00001233206,0.000004800586,0.00001135618,0.00002470936,0.000009054806,0.000492345],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0009530004,"threshold_uncertainty_score":0.001941621,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007826552432069613,"score_gpt":0.2176069644023715,"score_spread":0.2097804119703019,"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."}}