{"id":"W2753257100","doi":"10.3141/2658-01","title":"Modeling the Demand for New Transportation Services and Technologies","year":2017,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Microsimulation; Exploit; Scope (computer science); Computer science; Field (mathematics); Demand forecasting; Big data; Data science; Software; Operations research; Transport engineering; Engineering; Computer security","routes":{"ca_aff":true,"ca_fund":false,"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":["sts"],"consensus_categories":[],"category_scores_codex":[0.006089184,0.0002368032,0.000380822,0.0005156809,0.005848533,0.000707627,0.001843888,0.0003071258,0.000040443],"category_scores_gemma":[0.0003804773,0.0001643887,0.0002926152,0.0006980633,0.001329044,0.001439481,0.000004643102,0.001295121,0.00000333453],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001093,"about_ca_system_score_gemma":0.000838363,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.04192843,"about_ca_topic_score_gemma":0.3270971,"domain_scores_codex":[0.9944915,0.0005742963,0.001095503,0.0004259665,0.002592067,0.0008206708],"domain_scores_gemma":[0.9945438,0.0009528867,0.0006330335,0.000592927,0.002992221,0.0002851516],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.003904473,0.0003333492,0.6429633,0.0009990176,0.0005349657,0.00006853303,0.1128248,0.06529362,0.001089309,0.1007899,0.006978001,0.06422072],"study_design_scores_gemma":[0.002779426,0.0003927019,0.8786405,0.0007246389,0.000205146,3.112371e-7,0.04348192,0.004129433,0.0003924854,0.03151304,0.0373618,0.0003785434],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9483011,0.0007100479,0.02124601,0.02700443,0.0006539816,0.001724915,0.00009930808,0.0000849584,0.0001751926],"genre_scores_gemma":[0.9914135,0.003526879,0.003754535,0.00004666471,0.0002930666,0.00008689654,0.00003169811,0.00004295075,0.000803776],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2851687,"threshold_uncertainty_score":0.9954457,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1267950407008829,"score_gpt":0.4206037840298895,"score_spread":0.2938087433290065,"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."}}