{"id":"W3007388547","doi":"10.1002/itl2.153","title":"Dynamic wireless charging for CAEV taxi fleet in urban environment","year":2020,"lang":"en","type":"article","venue":"Internet Technology Letters","topic":"Energy Harvesting in Wireless Networks","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"Government of Ontario; Ministry of Energy","keywords":"Software deployment; Transport engineering; Wireless; Computer science; Fuel efficiency; Range (aeronautics); Vehicle-to-vehicle; Key (lock); Automotive engineering; Computer security; Telecommunications; Engineering; Computer network","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0000689568,0.0002440399,0.0002771085,0.0002153857,0.00001866861,0.00001904049,0.0004768502,0.000245676,0.00001878219],"category_scores_gemma":[0.00002473739,0.0002873062,0.00005707496,0.0002066107,0.0001210095,0.00007274379,0.000103504,0.0004996705,0.0000461762],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002120302,"about_ca_system_score_gemma":0.000003738835,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001128031,"about_ca_topic_score_gemma":0.00002721664,"domain_scores_codex":[0.9987453,0.00001317991,0.0003113592,0.0003651681,0.00008738568,0.0004775822],"domain_scores_gemma":[0.999584,0.00005315616,0.00004988227,0.000248898,0.00000509847,0.00005892571],"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":[0.00004792491,0.00005350935,0.02317626,0.0003321086,0.0002460807,0.0002508515,0.00134474,0.704435,0.2249876,0.003314091,0.02090483,0.02090701],"study_design_scores_gemma":[0.0006854305,0.00006021734,0.0003534447,0.0001745362,0.00001430902,0.0000147545,0.00005291198,0.9635544,0.01560732,0.00006565129,0.01896414,0.000452883],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.854355,0.0002697854,0.1360162,0.007582552,0.000274492,0.0002433586,0.000007743664,0.001166538,0.00008431197],"genre_scores_gemma":[0.9950133,0.00003418195,0.003276637,0.001251487,0.00006905029,0.0002113208,0.00002051147,0.0000892474,0.00003426851],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2591194,"threshold_uncertainty_score":0.9999579,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006312287046195986,"score_gpt":0.1825295908814794,"score_spread":0.1762173038352834,"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."}}