{"id":"W2783905471","doi":"10.1061/jtepbs.0000124","title":"Examining the Relationship between Drivers’ Anticipated Travel Time and Previous Experienced Travel Times","year":2018,"lang":"en","type":"article","venue":"Journal of Transportation Engineering Part A Systems","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Travel time; Time travel; Transport engineering; Psychology; Computer science; Engineering; Artificial intelligence","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":[],"consensus_categories":[],"category_scores_codex":[0.0009149184,0.0001123134,0.0002281372,0.0001400349,0.0003219841,0.0000766014,0.0001294439,0.0000987773,0.0000259243],"category_scores_gemma":[0.0001361932,0.00009255944,0.00004766907,0.0003155608,0.0001300413,0.0003478417,7.039767e-7,0.0001500841,0.000005031764],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002915017,"about_ca_system_score_gemma":0.00007969393,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007545749,"about_ca_topic_score_gemma":0.0000245813,"domain_scores_codex":[0.9986492,0.00009214675,0.000581771,0.0001148596,0.0003828842,0.0001791642],"domain_scores_gemma":[0.9988431,0.0004037234,0.0003389656,0.000074221,0.0002240739,0.0001158989],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00005255887,0.00003954677,0.6262515,0.00008867833,0.0001767199,0.00002202963,0.2964113,0.06828202,0.001163412,0.006689405,0.0005912279,0.0002315618],"study_design_scores_gemma":[0.0004612083,0.0001359007,0.9864246,0.0003102155,0.0001262543,0.000005437832,0.007653385,0.002880359,0.0001127259,0.000009158216,0.001715688,0.0001650812],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9660804,0.000174194,0.0326354,0.0001324354,0.0004978051,0.0002068372,0.00002122546,0.000044496,0.0002072498],"genre_scores_gemma":[0.9985908,0.00002313586,0.0005712479,0.000007778728,0.0004170149,0.00000567101,0.0000261185,0.000014491,0.0003437083],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3601731,"threshold_uncertainty_score":0.3774464,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05080946354533479,"score_gpt":0.2809122790018922,"score_spread":0.2301028154565574,"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."}}