{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004702514,0.000172158,0.0001943212,0.0002119968,0.0001423679,0.0002510654,0.0005985382,0.00008837937,0.0001007906],"category_scores_gemma":[0.00006164565,0.0001706733,0.00005653806,0.0004043889,0.00005540215,0.0001583993,0.00005778208,0.0002235159,0.0003314716],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001266429,"about_ca_system_score_gemma":0.00003792925,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001328822,"about_ca_topic_score_gemma":0.00004506516,"domain_scores_codex":[0.9985407,0.00006169769,0.00023498,0.0002538436,0.0004315892,0.0004772108],"domain_scores_gemma":[0.999186,0.0001526416,0.00003108694,0.0004361157,0.00009163437,0.0001024849],"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":[0.000198004,0.000248931,0.2324216,0.00160387,0.0004016807,0.000268677,0.001879542,0.2658062,0.02836657,0.0003347883,0.1771983,0.2912718],"study_design_scores_gemma":[0.0004845534,0.00007830492,0.02515424,0.0002982165,0.000006891797,0.000005596291,0.00001121914,0.8707676,0.0667906,0.00004264922,0.03600619,0.0003539442],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8305665,0.00004452644,0.06836998,0.00008555678,0.004518413,0.0003252932,0.000005234408,0.0009302677,0.09515423],"genre_scores_gemma":[0.9979486,0.000001244505,0.0003530115,0.0001470895,0.001407176,0.00001586869,0.000002723457,0.00006081586,0.00006345873],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6049614,"threshold_uncertainty_score":0.6959854,"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."}}