{"id":"W4225165293","doi":"10.1155/2022/3099721","title":"Improved F-DBSCAN for Trip End Identification Using Mobile Phone Data in Combination with Base Station Density","year":2022,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Human Mobility and Location-Based Analysis","field":"Social Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Fundamental Research Funds for the Central Universities; Natural Science Foundation of Shaanxi Province; National Natural Science Foundation of China","keywords":"DBSCAN; Cluster analysis; Identification (biology); Computer science; Mobile phone; Base station; Data mining; Phone; Real-time computing; Artificial intelligence; Correlation clustering; CURE data clustering algorithm; Computer network; Telecommunications","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":[],"consensus_categories":[],"category_scores_codex":[0.0008902653,0.001375601,0.001223321,0.00316347,0.000963821,0.001416821,0.001887554,0.000912542,0.001147214],"category_scores_gemma":[0.003523749,0.0004534914,0.0009892224,0.003256668,0.0003839398,0.001410952,0.0008684199,0.001011173,0.0006938996],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001074106,"about_ca_system_score_gemma":0.001674562,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02831007,"about_ca_topic_score_gemma":0.02490321,"domain_scores_codex":[0.9985812,0.0002384124,0.00008776415,0.0003327953,0.0006202533,0.0001397207],"domain_scores_gemma":[0.9982981,0.000412059,0.0001254886,0.0001878896,0.0009220063,0.00005452294],"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":[0.0004463034,0.0001785695,0.01015056,0.0001648391,0.0002165887,0.0002142588,0.0001943047,0.3866622,0.01126493,0.003834171,0.003716892,0.5829564],"study_design_scores_gemma":[0.00001305365,0.00004400614,0.002261235,0.000006677637,0.00002646907,0.0001300606,0.00007456511,0.9873585,0.007267931,0.00130733,0.001471223,0.00003889577],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06580435,0.0004496312,0.9273288,0.0001717783,0.00009685271,0.00009730497,0.0004107223,0.002929647,0.002710923],"genre_scores_gemma":[0.4525245,0.0003502509,0.5428879,0.0001028344,0.00004933404,0.000166733,0.001178562,0.0001906528,0.002549344],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02831007,"threshold_uncertainty_score":0.05629057,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02645584142354782,"score_gpt":0.3194467377961446,"score_spread":0.2929908963725968,"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."}}