{"id":"W4408789181","doi":"10.1016/j.commtr.2025.100172","title":"Modular AI agents for transportation surveys and interviews: Advancing engagement, transparency, and cost efficiency","year":2025,"lang":"en","type":"article","venue":"Communications in Transportation Research","topic":"Transportation and Mobility Innovations","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Environment and Climate Change Canada; McGill University; University of Sydney; Concordia University; National Research Foundation Singapore; Singapore-MIT Alliance for Research and Technology Centre; Massachusetts Institute of Technology","keywords":"Transparency (behavior); Modular design; Business; Computer science; Computer security","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003014105,0.0001684065,0.0002272932,0.000716083,0.0003663545,0.0000575213,0.0003899894,0.0001116669,0.00002318728],"category_scores_gemma":[0.00005957191,0.0002014166,0.00004680397,0.001302047,0.0002620015,0.0003182214,0.000005301752,0.0005177098,0.000001359509],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008524365,"about_ca_system_score_gemma":0.00007578384,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002001142,"about_ca_topic_score_gemma":0.01435349,"domain_scores_codex":[0.9980693,0.0003252342,0.000744212,0.0003123937,0.0002084399,0.0003404342],"domain_scores_gemma":[0.998417,0.0005175196,0.00003589894,0.0006170372,0.0003388475,0.00007374513],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001141466,0.001485851,0.334341,0.005252941,0.000385655,0.000007900984,0.04558778,0.0655816,0.01301056,0.2369033,0.003052742,0.2942765],"study_design_scores_gemma":[0.001603293,0.00004350841,0.9252326,0.0002775248,0.00004370747,1.399107e-7,0.002100207,0.03973762,0.0004922085,0.002313705,0.0279109,0.0002445958],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4435027,0.002183293,0.5476106,0.002103579,0.0001439628,0.002782975,0.0006333875,0.0002111072,0.0008284572],"genre_scores_gemma":[0.9896311,0.003672536,0.003782167,0.00008326168,0.000004905689,0.00127352,0.001435938,0.00002457008,0.00009205163],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5908915,"threshold_uncertainty_score":0.8213528,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1470134943528009,"score_gpt":0.4417383472024958,"score_spread":0.2947248528496949,"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."}}