{"id":"W3140979475","doi":"10.1155/2021/6646768","title":"Analysis of Travel Hot Spots of Taxi Passengers Based on Community Detection","year":2021,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Human Mobility and Location-Based Analysis","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Hot spot (computer programming); Geography; Transport engineering; Rush hour; Evening; Computer science; Engineering","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":[0.0004174938,0.0004360174,0.0003658925,0.005056859,0.0004125912,0.0005199515,0.0003184264,0.0002596284,0.001027405],"category_scores_gemma":[0.001225328,0.0001225142,0.0004528547,0.002358854,0.000171795,0.0005284331,0.0006398634,0.0001813645,0.0002449567],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003239068,"about_ca_system_score_gemma":0.0004189528,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01185738,"about_ca_topic_score_gemma":0.016539,"domain_scores_codex":[0.9996972,0.00004828542,0.00002277618,0.00008683348,0.00007463075,0.00007036424],"domain_scores_gemma":[0.9992632,0.0001568246,0.0001491588,0.00004808886,0.0002774156,0.0001052812],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004533345,0.0002237226,0.8790932,0.0002072463,0.0001946678,0.0004166638,0.001498203,0.00781051,0.01194961,0.0007583136,0.001369505,0.09602512],"study_design_scores_gemma":[0.00001920402,0.0002207162,0.8484316,0.00003994272,0.0001570399,0.0005455298,0.003212354,0.1407233,0.004071828,0.0007043114,0.001808873,0.00006529278],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9866555,0.00009375513,0.01096148,0.00002665864,0.00001156726,0.00008143015,0.0009692654,0.0001165181,0.001083879],"genre_scores_gemma":[0.9925703,0.00004367861,0.006070997,0.000004167442,0.000006979085,0.00004590825,0.0008308679,0.000006211093,0.0004208506],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01185738,"threshold_uncertainty_score":0.02357674,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0186361091800118,"score_gpt":0.2992111457628605,"score_spread":0.2805750365828487,"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."}}