{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006429245,0.0007448288,0.0003204183,0.00056834,0.0003416685,0.0005973465,0.0009119983,0.0003720754,0.002592592],"category_scores_gemma":[0.001845128,0.000200123,0.0002316374,0.0003837234,0.0002362106,0.0007229411,0.0003954736,0.0003044141,0.0002816461],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001292931,"about_ca_system_score_gemma":0.0007851124,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006642683,"about_ca_topic_score_gemma":0.0049547,"domain_scores_codex":[0.9995878,0.0001279087,0.00002130511,0.00007299719,0.0001132328,0.00007678414],"domain_scores_gemma":[0.9989213,0.0003883844,0.0001199065,0.0001157165,0.0003843268,0.00007049944],"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":[0.0008627264,0.0003506264,0.008699482,0.0002783756,0.00008208882,0.0001649565,0.0001078565,0.8725736,0.02193864,0.001427621,0.001147299,0.09236667],"study_design_scores_gemma":[0.0000436812,0.000715312,0.003057774,0.00001132802,0.00005603371,0.00003556964,0.00008182464,0.9843711,0.01050961,0.0003619525,0.0007453797,0.00001049389],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9138231,0.000497911,0.07881106,0.0001461304,0.00006744272,0.0001942242,0.0001213315,0.001846683,0.004492132],"genre_scores_gemma":[0.9917972,0.00004972166,0.007573013,0.000009821179,0.000003050586,0.00002724949,0.00004414149,0.00001441861,0.0004813351],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006642683,"threshold_uncertainty_score":0.01320803,"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."}}