{"id":"W4405244688","doi":"10.1080/23249935.2024.2438307","title":"Developing CUSTOM framework: explore telecommuting-induced activity-travel demands with mode choice","year":2024,"lang":"en","type":"article","venue":"Transportmetrica A Transport Science","topic":"Urban and Freight Transport Logistics","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Telecommuting; Computer science; Work (physics); Travel behavior; Mode choice; Predictability; Mode (computer interface); Scheduling (production processes); Journey to work; Operations research; Econometrics; Transport engineering; Economics; Operations management; Engineering; Human–computer interaction; Public transport","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":[0.001689534,0.000619717,0.0007625584,0.0008162421,0.0002874873,0.001164851,0.001730356,0.00103304,0.004821945],"category_scores_gemma":[0.005314062,0.0005758469,0.001122843,0.001276862,0.0006833526,0.00109886,0.001337,0.001063299,0.0003569203],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001802037,"about_ca_system_score_gemma":0.001597597,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06966618,"about_ca_topic_score_gemma":0.05878368,"domain_scores_codex":[0.9992673,0.0003819187,0.00002213847,0.0001649876,0.00005555434,0.0001081498],"domain_scores_gemma":[0.9979254,0.001322173,0.0002977971,0.0001797628,0.0001386957,0.0001361722],"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":[0.00003823407,0.00005556338,0.00825291,0.00002419275,0.0000603735,0.00006324603,0.00007883385,0.965925,0.0001611207,0.02052308,0.0005562737,0.004261174],"study_design_scores_gemma":[0.000006004564,0.00001827502,0.001281672,0.000004523435,0.000009916009,0.00001272244,0.00003487477,0.9929141,0.0000375234,0.00507244,0.0005991336,0.000008840137],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3726444,0.0004165755,0.6096769,0.001056217,0.0000726312,0.0001490271,0.002689186,0.0004830839,0.01281193],"genre_scores_gemma":[0.9574833,0.0001871856,0.03671207,0.00007455108,0.00003584944,0.0001039362,0.0008930871,0.00007174938,0.004438248],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.06966618,"threshold_uncertainty_score":0.1385214,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06609534214874034,"score_gpt":0.2829369695781594,"score_spread":0.2168416274294191,"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."}}