{"id":"W2901541875","doi":"10.1109/access.2018.2880972","title":"Towards Smart Parking Based on Fog Computing","year":2018,"lang":"en","type":"article","venue":"IEEE Access","topic":"Smart Parking Systems Research","field":"Engineering","cited_by":97,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Provisioning; Cloud computing; Parking guidance and information; Fog computing; Process (computing); Wireless ad hoc network; Computer network; Transport engineering; Wireless; Telecommunications; Engineering","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0002957419,0.0003014682,0.0002906465,0.0002799899,0.0004435824,0.0008853311,0.0007880792,0.0006676348,0.0007237176],"category_scores_gemma":[0.0004305608,0.0001990197,0.0004052576,0.0003857478,0.0004184076,0.0014226,0.0009405185,0.0006561583,0.0003058716],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002985194,"about_ca_system_score_gemma":0.0005788493,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00253076,"about_ca_topic_score_gemma":0.002945922,"domain_scores_codex":[0.999822,0.00003805964,0.000007462408,0.00003540512,0.00004485976,0.00005228188],"domain_scores_gemma":[0.9998612,0.00002768629,0.00001005811,0.00002946566,0.00004913547,0.00002246603],"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":[0.0004852348,0.0003152696,0.005632028,0.0006096351,0.0001744659,0.001405506,0.0007127305,0.2048866,0.06679388,0.2344515,0.02639345,0.4581397],"study_design_scores_gemma":[0.00002680844,0.0001211562,0.001083024,0.00004696278,0.00004771344,0.0004077192,0.000252918,0.8764822,0.01032319,0.07514285,0.03602668,0.00003886689],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07522584,0.003771936,0.8952024,0.001948067,0.0005295028,0.0001418279,0.00007633906,0.001296586,0.02180753],"genre_scores_gemma":[0.8598856,0.001791758,0.134136,0.0006249518,0.0001033243,0.00005537534,0.00008300046,0.00004391279,0.003275997],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00253076,"threshold_uncertainty_score":0.005032063,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04976527314613582,"score_gpt":0.336431413722726,"score_spread":0.2866661405765902,"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."}}