{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001135186,0.00007441645,0.000375837,0.0004343397,0.0001917659,0.000008119661,0.0001181176,0.0000699205,0.0001586234],"category_scores_gemma":[0.0002448519,0.00007629037,0.0004208593,0.00164941,0.0001199036,0.000188821,5.605565e-7,0.0002336064,2.567643e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000843715,"about_ca_system_score_gemma":0.0002542556,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00107226,"about_ca_topic_score_gemma":0.0364026,"domain_scores_codex":[0.9981694,0.0004569727,0.0006584576,0.00008471381,0.0005347657,0.00009564182],"domain_scores_gemma":[0.9976552,0.0004232797,0.0007755932,0.0001676389,0.0009101902,0.00006808595],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0004357323,0.0008032303,0.0152954,0.00008870198,0.0006792533,0.000008378457,0.02235327,0.8818802,0.04197104,0.0002929661,0.000001823921,0.03619],"study_design_scores_gemma":[0.0006224989,0.0001976114,0.9298473,0.00007626406,0.001544056,1.08386e-7,0.02623987,0.001641901,0.03939082,0.0002621507,0.00008355505,0.00009387274],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9676005,0.00003670532,0.03187729,0.0001527869,0.00007728847,0.00005375357,0.00002503853,0.000004423227,0.0001722324],"genre_scores_gemma":[0.9991727,0.00006692234,0.0006288373,0.00003793947,0.00002547251,0.000001507026,0.00004620529,0.000004553492,0.000015793],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9145519,"threshold_uncertainty_score":0.9811805,"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."}}