{"id":"W3093207174","doi":"10.1002/spe.2914","title":"Evaluating system architectures for driving range estimation and charge planning for electric vehicles","year":2020,"lang":"en","type":"article","venue":"Software Practice and Experience","topic":"Electric Vehicles and Infrastructure","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Canadian Institute of Steel Construction","keywords":"Software deployment; Computer science; Range (aeronautics); Software; Latency (audio); Real-time computing; Cloud computing; Process (computing); Simulation; Embedded system; Engineering; Telecommunications","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001687066,0.0001365241,0.000154073,0.00003803616,0.0002356092,0.00009628303,0.0000668568,0.00006262316,0.000001359959],"category_scores_gemma":[0.001280317,0.0001291114,0.00002482602,0.000132391,0.00001455572,0.0002319116,0.00001778504,0.0001289848,5.092722e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002396948,"about_ca_system_score_gemma":0.00001461148,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003483114,"about_ca_topic_score_gemma":1.883067e-7,"domain_scores_codex":[0.9992306,0.00001980776,0.0001702763,0.0002235976,0.0001113633,0.0002443097],"domain_scores_gemma":[0.9990219,0.0007026865,0.00006782548,0.0000652102,0.00005534226,0.00008707726],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004060463,0.00001513214,0.005522228,0.002979459,0.0001177252,0.00000819203,0.06651678,0.0428343,0.07940721,0.000646228,0.0006220498,0.8009247],"study_design_scores_gemma":[0.0004668784,0.0002662563,0.0006229191,0.00008667274,0.00004502692,0.00004996063,0.001678683,0.9889106,0.006660327,0.0001092539,0.0008963138,0.0002070792],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7781193,0.002971095,0.2179229,0.0002135647,0.00005022345,0.0004347253,0.000005186638,0.0002639021,0.00001916238],"genre_scores_gemma":[0.9350686,0.0000266619,0.06433113,0.0002166539,0.0001419693,0.0001835265,0.000003994984,0.00002531932,0.000002116497],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9460763,"threshold_uncertainty_score":0.526501,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02430658282828406,"score_gpt":0.3061994892382636,"score_spread":0.2818929064099795,"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."}}