{"id":"W4391094969","doi":"10.1109/bigdata59044.2023.10386990","title":"Identifying Regions of High Demand for Transportation Services based on Cluster Evolution and Graph Analysis","year":2023,"lang":"en","type":"article","venue":"","topic":"Human Mobility and Location-Based Analysis","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Cluster (spacecraft); Graph; Graph theory; Theoretical computer science; Mathematics; Combinatorics; Computer network","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004973512,0.0004114072,0.0003381691,0.005576526,0.0005816674,0.001286352,0.0006001485,0.0004910355,0.001190134],"category_scores_gemma":[0.002169945,0.0001733728,0.0008317335,0.003279024,0.0004772239,0.0008958085,0.0007550792,0.0003504299,0.0001755717],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001599228,"about_ca_system_score_gemma":0.0007800923,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03969914,"about_ca_topic_score_gemma":0.04179643,"domain_scores_codex":[0.9995389,0.0001099625,0.00002059653,0.000117842,0.000130238,0.00008247331],"domain_scores_gemma":[0.9986957,0.0005896528,0.0002176319,0.00008823346,0.0003287676,0.00008005519],"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":[0.0005119877,0.0003558599,0.2728362,0.0003908448,0.0004281894,0.00133099,0.002681985,0.4084079,0.02286625,0.04985253,0.00727645,0.2330609],"study_design_scores_gemma":[0.0000104769,0.00004501385,0.06722592,0.00002448059,0.00007293242,0.0001979911,0.001303822,0.914101,0.002876888,0.01139123,0.002712197,0.0000381075],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6170082,0.0003207942,0.3717388,0.0004832958,0.00002369454,0.0003058396,0.002133228,0.0009594126,0.007026732],"genre_scores_gemma":[0.9025697,0.0001248192,0.09431794,0.00003176219,0.000008283767,0.00006604611,0.00157257,0.00005945463,0.001249428],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03969914,"threshold_uncertainty_score":0.0789361,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02210452587663865,"score_gpt":0.3061299366257683,"score_spread":0.2840254107491296,"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."}}