{"id":"W4416187061","doi":"10.32920/30605294.v1","title":"Generative AI Agents for Travel Behaviour: Applications in Surveys and Modelling","year":2025,"lang":"","type":"article","venue":"","topic":"Human Mobility and Location-Based Analysis","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Generative grammar; Benchmark (surveying); Reliability (semiconductor); Generative model; Human intelligence; Ranging","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003757043,0.0005713908,0.0004481123,0.0009744555,0.0005494915,0.001389307,0.002091012,0.00132561,0.005580307],"category_scores_gemma":[0.02749693,0.0004149264,0.0009249561,0.0009826944,0.0009623304,0.001705577,0.00155937,0.001518528,0.000824831],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00153312,"about_ca_system_score_gemma":0.001145121,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01602776,"about_ca_topic_score_gemma":0.01999655,"domain_scores_codex":[0.9982556,0.001258226,0.00007511456,0.0002099987,0.0001594205,0.00004161197],"domain_scores_gemma":[0.9816086,0.01546163,0.0005556443,0.001431863,0.0006664632,0.0002759497],"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":[0.0001264457,0.0002271629,0.009226294,0.0002194787,0.000111929,0.000160072,0.001192073,0.8725706,0.001185625,0.04183408,0.003820767,0.06932539],"study_design_scores_gemma":[0.00001547251,0.00001927966,0.0003834385,0.00001441629,0.000006509645,0.00002083579,0.00007633827,0.9835176,0.0003902451,0.01341357,0.002129119,0.00001313606],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.06588877,0.0001746458,0.9232473,0.001147168,0.00009039853,0.0003409582,0.0009620905,0.002858734,0.005289869],"genre_scores_gemma":[0.5212417,0.0001715636,0.4736497,0.0002940284,0.00004087154,0.0007104289,0.001067069,0.0002565336,0.002568139],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01602776,"threshold_uncertainty_score":0.03186893,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05151600339919887,"score_gpt":0.3578446164287833,"score_spread":0.3063286130295845,"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."}}