{"id":"W3014605128","doi":"10.1155/2020/8956910","title":"Recognition of Functional Areas Based on Call Detail Records and Point of Interest Data","year":2020,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Human Mobility and Location-Based Analysis","field":"Social Sciences","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Beijing Municipal Natural Science Foundation; Beijing Municipal Science and Technology Commission","keywords":"Beijing; Identification (biology); Computer science; Big data; Point of interest; Point (geometry); Cluster analysis; Data mining; Kernel density estimation; Reliability (semiconductor); Transport engineering; Data science; Geography; Artificial intelligence; Engineering; Statistics; Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004768136,0.00005692639,0.0001792565,0.00009139709,0.0000572173,0.000006538568,0.0001088478,0.00004091742,0.0003091],"category_scores_gemma":[0.0004584167,0.00005442505,0.00007053688,0.0002123652,0.00010259,0.0003523564,0.000001479276,0.0001142092,0.000001271667],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002441308,"about_ca_system_score_gemma":0.0001759994,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001294996,"about_ca_topic_score_gemma":0.004791503,"domain_scores_codex":[0.9989785,0.00008906343,0.0004939004,0.0001176005,0.0002605451,0.00006042326],"domain_scores_gemma":[0.9986703,0.0002318711,0.0005171775,0.00008825901,0.0004071646,0.00008519674],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.008996163,0.001860574,0.06866882,0.001207222,0.0005426675,0.00003722097,0.05167989,0.221453,0.01918259,0.001535868,0.001687935,0.6231481],"study_design_scores_gemma":[0.007527289,0.003784455,0.9038197,0.001924517,0.001333195,0.000002247187,0.03629726,0.01670788,0.009449407,0.01089459,0.007548771,0.0007106306],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9762881,0.00005276569,0.02072529,0.002330563,0.00009467982,0.0001087096,0.0002255283,0.000006889539,0.0001673997],"genre_scores_gemma":[0.9977718,0.00006867552,0.001665544,0.0001361375,0.00008625315,0.000001086542,0.0002603813,0.000004112414,0.000005985848],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.835151,"threshold_uncertainty_score":0.3384428,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09713570267979926,"score_gpt":0.3077895594093624,"score_spread":0.2106538567295631,"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."}}