{"id":"W2739260306","doi":"10.3141/2668-05","title":"Identification of Representative Patterns of Time Use Activity Through Fuzzy <i>C</i> -Means Clustering","year":2017,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"Saint Mary's University; Dalhousie University","funders":"","keywords":"Cluster analysis; Hierarchical clustering; Computer science; Data mining; Initialization; Identification (biology); Artificial intelligence; Machine learning","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0009309062,0.000657413,0.0006812296,0.003003852,0.0006933245,0.0008943293,0.0009704747,0.0005367105,0.0009463621],"category_scores_gemma":[0.00253949,0.0002448474,0.0009326658,0.002515704,0.0003593654,0.0005626649,0.0004007362,0.0005636233,0.0003107621],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001112723,"about_ca_system_score_gemma":0.001446558,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04570171,"about_ca_topic_score_gemma":0.03414177,"domain_scores_codex":[0.999403,0.00008336589,0.00004864308,0.0001954229,0.0001888725,0.00008073829],"domain_scores_gemma":[0.9990563,0.0002733207,0.000139285,0.00008921435,0.0004044619,0.00003740944],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003971633,0.0003624544,0.03591126,0.0002458589,0.0001583401,0.0002584477,0.001411659,0.4171108,0.0223636,0.008263795,0.004784661,0.5087318],"study_design_scores_gemma":[0.000007769449,0.00003002169,0.01164625,0.0000144964,0.00001929902,0.00005431096,0.0001921946,0.9800926,0.004077103,0.002418245,0.001411868,0.00003569246],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1735546,0.0001097908,0.8220904,0.0001012769,0.00002177031,0.0003106428,0.0007065987,0.0008074029,0.002297574],"genre_scores_gemma":[0.5393661,0.0001006875,0.4569352,0.00002607701,0.00001245717,0.0003034389,0.00152625,0.00008239139,0.001647366],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.04570171,"threshold_uncertainty_score":0.09087139,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1373840106794582,"score_gpt":0.4361215299779845,"score_spread":0.2987375192985264,"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."}}