{"id":"W2976735552","doi":"10.1109/icdim.2018.8847135","title":"Mining Trajectory Data and Identifying Patterns for Taxi Movement Trips","year":2018,"lang":"en","type":"article","venue":"","topic":"Human Mobility and Location-Based Analysis","field":"Social Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Trajectory; Cluster analysis; Computer science; Geographic coordinate system; Global Positioning System; TRIPS architecture; Data mining; Identification (biology); Point (geometry); Scale (ratio); Urban computing; Geography; Artificial intelligence; Machine learning; Mathematics; Cartography","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001229323,0.00004892737,0.00008216528,0.00004739773,0.0006230598,0.0001103844,0.0002436299,0.00003337486,0.0009353973],"category_scores_gemma":[0.0001831947,0.00004645197,0.00002609266,0.00009299058,0.0001492317,0.0001882942,0.0000497416,0.00002436917,0.000008224869],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000306587,"about_ca_system_score_gemma":0.00007038265,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.006724684,"about_ca_topic_score_gemma":0.13309,"domain_scores_codex":[0.999226,0.00006707256,0.0001415526,0.0002481358,0.0001598611,0.000157354],"domain_scores_gemma":[0.9994152,0.0001579358,0.00004238157,0.0002554776,0.00006589205,0.00006313063],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"qualitative","study_design_scores_codex":[0.00005866034,0.0003894918,0.2137567,0.0002513682,0.0003912671,0.000002073181,0.1610334,0.00001091045,0.001339452,0.01828137,0.02851875,0.5759665],"study_design_scores_gemma":[0.002838694,0.0004471651,0.08866586,0.0002115964,0.0006241242,3.2381e-7,0.4364205,0.06109894,0.003374998,0.009763745,0.3951443,0.001409728],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8366451,0.00008689114,0.1552447,0.001357688,0.0002055928,0.0003741605,0.00005584753,0.00006950067,0.005960518],"genre_scores_gemma":[0.9957448,0.00001857106,0.0009659504,0.0004954699,0.0003690411,0.00001573477,0.00003680497,0.000003912801,0.002349733],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5745568,"threshold_uncertainty_score":0.9999779,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.126000276287438,"score_gpt":0.3836326161494825,"score_spread":0.2576323398620445,"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."}}