{"id":"W2955336013","doi":"10.1007/s11027-019-09870-9","title":"Wireless charging and shared autonomous battery electric vehicles (W+SABEV): synergies that accelerate sustainable mobility and greenhouse gas emission reduction","year":2019,"lang":"en","type":"article","venue":"Mitigation and Adaptation Strategies for Global Change","topic":"Electric Vehicles and Infrastructure","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Argonne National Laboratory; Horace H. Rackham School of Graduate Studies, University of Michigan; Canada Excellence Research Chairs, Government of Canada; U.S. Department of Energy","keywords":"Greenhouse gas; Software deployment; Wireless; Battery (electricity); Sustainability; Battery electric vehicle; Computer science; Sustainable transport; Emerging technologies; Environmental economics; Telecommunications; Economics","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":[0.0002293726,0.0003236796,0.0001824212,0.0002094589,0.0002125099,0.0006716557,0.0004538517,0.0003284756,0.003569062],"category_scores_gemma":[0.0005054997,0.00007254998,0.0002084484,0.0004817085,0.0003076111,0.001418374,0.0009687209,0.0002659565,0.0004214104],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000178746,"about_ca_system_score_gemma":0.0003859841,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007807178,"about_ca_topic_score_gemma":0.001856503,"domain_scores_codex":[0.9998955,0.00002265197,0.000003709124,0.00001917281,0.00002730857,0.00003175096],"domain_scores_gemma":[0.9998691,0.00004388137,0.00001802145,0.00001988601,0.00003175739,0.00001740002],"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":[0.0008946917,0.0003383013,0.006387047,0.0004511873,0.0001853363,0.0004197159,0.0001671278,0.1517095,0.04225525,0.1052525,0.007231084,0.6847082],"study_design_scores_gemma":[0.000220437,0.002572875,0.008883982,0.0001247161,0.0003300069,0.001431426,0.001343686,0.6482378,0.06061063,0.169237,0.106899,0.0001084759],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6343249,0.004062595,0.2731136,0.002411626,0.001064876,0.00009940689,0.0002925252,0.001029531,0.083601],"genre_scores_gemma":[0.9910758,0.0004290101,0.00363103,0.00007722215,0.00003638145,0.000008768881,0.0000410342,0.00001463053,0.004686111],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003569062,"threshold_uncertainty_score":0.0119397,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02003710818829803,"score_gpt":0.227675689397411,"score_spread":0.2076385812091129,"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."}}