{"id":"W4382284859","doi":"10.1002/cjas.1734","title":"Research trends in the application of big data in smart cities—A literature review","year":2023,"lang":"en","type":"article","venue":"Canadian Journal of Administrative Sciences / Revue Canadienne des Sciences de l Administration","topic":"Smart Cities and Technologies","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Big data; Data science; Context (archaeology); Smart city; Intersection (aeronautics); Space (punctuation); Perspective (graphical); Computer science; Internet of Things; Engineering; Geography; World Wide Web; Data mining; Transport engineering; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.009075323,0.0007105004,0.001168802,0.02782446,0.001004504,0.005421161,0.001042322,0.001802246,0.002702581],"category_scores_gemma":[0.02649923,0.0005874447,0.00136761,0.03785411,0.001509274,0.00596437,0.001388152,0.001720724,0.0005751306],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003105896,"about_ca_system_score_gemma":0.009402161,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005028655,"about_ca_topic_score_gemma":0.008859091,"domain_scores_codex":[0.9955115,0.00127261,0.0009285706,0.0005579663,0.001473504,0.000255833],"domain_scores_gemma":[0.9124683,0.06652582,0.00557293,0.0007134934,0.01346764,0.001251852],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001920656,0.000124951,0.01553806,0.2077819,0.0008904245,0.000811654,0.004213264,0.001418739,0.001449874,0.02395039,0.04540218,0.6982265],"study_design_scores_gemma":[0.00002561977,0.0001579838,0.0282669,0.2212031,0.00206252,0.001500266,0.0139737,0.001122802,0.001257998,0.007824292,0.7224677,0.0001372325],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.002405613,0.9893491,0.000505209,0.005090904,0.0007085141,0.0000214132,0.000180685,0.00001208401,0.001726381],"genre_scores_gemma":[0.01447634,0.9823151,0.0008197422,0.001296972,0.0006843999,0.0000288596,0.0001771044,0.00001039482,0.0001912141],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9721755,"threshold_uncertainty_score":0.04799551,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2219398805110095,"score_gpt":0.3773695315737625,"score_spread":0.155429651062753,"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."}}