{"id":"W4391526845","doi":"10.5198/jtlu.2024.2278","title":"Will you ride the train? A combined home-work spatial segmentation approach","year":2024,"lang":"en","type":"article","venue":"Journal of Transport and Land Use","topic":"Human Mobility and Location-Based Analysis","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"Canadian Institutes of Health Research; Natural Sciences and Engineering Research Council of Canada; Government of Canada; California HIV/AIDS Research Program","keywords":"Transport engineering; Work (physics); Segmentation; Computer science; Engineering; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009370287,0.00006287907,0.0001348075,0.0000704573,0.0002616614,0.0001286793,0.00009230749,0.00004603586,0.000115603],"category_scores_gemma":[0.00001433585,0.00003782596,0.0001264559,0.0002251621,0.000120028,0.0003463817,0.00000153581,0.0001618604,9.524364e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002359575,"about_ca_system_score_gemma":0.0001159441,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001752426,"about_ca_topic_score_gemma":0.003350871,"domain_scores_codex":[0.9991972,0.00008890179,0.0002477452,0.00008524764,0.0002775657,0.0001033116],"domain_scores_gemma":[0.9996409,0.00009907929,0.00006785165,0.00005603585,0.00005959917,0.0000764827],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000550401,0.0004536608,0.753229,0.000175907,0.0006992062,0.00009636298,0.1870455,0.001652879,0.00005643842,0.003497466,0.00103017,0.05151293],"study_design_scores_gemma":[0.002006416,0.0003468993,0.8968411,0.0002504935,0.001468718,0.00001448341,0.01766509,0.002333184,0.00003686632,0.002925093,0.07568898,0.0004226817],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9852681,0.000442879,0.01192382,0.001839653,0.0001678732,0.000101726,0.000007962809,0.00001364086,0.0002344076],"genre_scores_gemma":[0.9987738,0.000297138,0.0001098635,0.00008276437,0.0003238301,0.000001953916,0.00001241686,0.000004526222,0.0003937323],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1693805,"threshold_uncertainty_score":0.2649156,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0194277183003346,"score_gpt":0.2647000013963888,"score_spread":0.2452722830960542,"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."}}