{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001508961,0.0007945906,0.000727507,0.002904236,0.0006788226,0.001568351,0.00122618,0.0007420315,0.004092644],"category_scores_gemma":[0.003490031,0.0004533795,0.002006568,0.002587656,0.000834549,0.001026853,0.001776441,0.000724823,0.0006784426],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001935641,"about_ca_system_score_gemma":0.002491654,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1371471,"about_ca_topic_score_gemma":0.1581144,"domain_scores_codex":[0.9985421,0.0007700376,0.00004389323,0.0002891058,0.0001546165,0.0002002207],"domain_scores_gemma":[0.9984847,0.0007456032,0.0002594566,0.0001339667,0.0002091111,0.0001671523],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005237726,0.0003324764,0.8807115,0.0001774374,0.001154473,0.0003953903,0.002957707,0.02982212,0.001056607,0.008215946,0.0020159,0.07263663],"study_design_scores_gemma":[0.00005934602,0.0004956022,0.6316479,0.000159009,0.0007468942,0.0002259672,0.007101797,0.3371665,0.000763903,0.01469499,0.006847057,0.00009093603],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8936856,0.001154066,0.09107884,0.001869602,0.00005182702,0.000428112,0.00254711,0.0001980721,0.008986835],"genre_scores_gemma":[0.9816403,0.0001934193,0.01463713,0.0001023028,0.00002328253,0.0001870009,0.001102356,0.00001875821,0.002095479],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1371471,"threshold_uncertainty_score":0.2726976,"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."}}