{"id":"W3088340659","doi":"10.32866/001c.17291","title":"Long-Distance Person Travel: A Cluster-Based Approach","year":2020,"lang":"en","type":"article","venue":"Findings","topic":"Urban Transport and Accessibility","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"TRIPS architecture; Market segmentation; Segmentation; Cluster analysis; Geography; Set (abstract data type); Business; Transport engineering; Marketing; Computer science; Artificial intelligence; Engineering","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":[0.00239319,0.001030373,0.001080087,0.006589765,0.001658319,0.003669563,0.002522841,0.001534363,0.005046434],"category_scores_gemma":[0.005988062,0.0005986651,0.002416206,0.008420424,0.0006268859,0.001902738,0.002227062,0.001595893,0.001404033],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002670433,"about_ca_system_score_gemma":0.002655334,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.09746857,"about_ca_topic_score_gemma":0.09694937,"domain_scores_codex":[0.9981159,0.0007339965,0.0001113682,0.0005572933,0.0002931491,0.0001883294],"domain_scores_gemma":[0.9976996,0.001116276,0.0001619268,0.0002207695,0.000640474,0.0001611106],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006813787,0.0007176506,0.08431949,0.0008913616,0.002009626,0.0005984496,0.006127404,0.5924827,0.002159978,0.0374997,0.02398514,0.2485272],"study_design_scores_gemma":[0.0000227452,0.00008262563,0.02095065,0.0001439905,0.0002225669,0.0001397536,0.003465246,0.9452357,0.0004500831,0.01857856,0.01062788,0.00008015029],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1919142,0.0008815068,0.7795198,0.001197559,0.0001827738,0.001762636,0.01168104,0.001784824,0.01107571],"genre_scores_gemma":[0.5803524,0.0004997225,0.4007851,0.0001156285,0.00008403033,0.0007514777,0.01081415,0.0003374739,0.006259827],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.09746857,"threshold_uncertainty_score":0.1938025,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04879126107022032,"score_gpt":0.279825491675714,"score_spread":0.2310342306054937,"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."}}