{"id":"W2045233237","doi":"10.1002/atr.5670410106","title":"Using a k‐means clustering algorithm to examine patterns of pedestrian involved crashes in Honolulu, Hawaii","year":2007,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Urban Transport and Accessibility","field":"Social Sciences","cited_by":94,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Cluster analysis; Pedestrian; Computer science; Data mining; Hierarchical clustering; Machine learning; Transport engineering; 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":[0.0003719727,0.0002005593,0.0001729082,0.002170907,0.0006079221,0.0006377803,0.0003715905,0.0001953874,0.000663249],"category_scores_gemma":[0.001857117,0.0001086315,0.000210248,0.0018759,0.0001720412,0.000269528,0.0003514408,0.0001160293,0.0001071704],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007768784,"about_ca_system_score_gemma":0.0008132577,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.2070961,"about_ca_topic_score_gemma":0.2717121,"domain_scores_codex":[0.9998208,0.00005817509,0.00001995477,0.00003270446,0.00004427091,0.00002424352],"domain_scores_gemma":[0.9992483,0.0002185504,0.00009888713,0.00004121796,0.0003295081,0.00006351776],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001990139,0.000121404,0.8936688,0.0001172563,0.0002592884,0.0004995271,0.005329438,0.01369424,0.004846484,0.0004112285,0.001457863,0.07939544],"study_design_scores_gemma":[0.00000969567,0.00009583713,0.9332292,0.00002306619,0.00006641958,0.0001067887,0.009353478,0.05435948,0.001260762,0.0002299743,0.0012284,0.00003678803],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9967945,0.00004357256,0.002211994,0.00002835418,0.000003032451,0.00003096323,0.0001673917,0.00003418207,0.0006858866],"genre_scores_gemma":[0.9954732,0.00003428445,0.003779593,0.000004787566,0.000001636227,0.00003149204,0.0002926131,0.000004961134,0.0003774903],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2070961,"threshold_uncertainty_score":0.4117814,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03934509033974769,"score_gpt":0.333849249030034,"score_spread":0.2945041586902863,"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."}}