{"id":"W1990305734","doi":"10.3141/2183-04","title":"Augmenting Transit Trip Characterization and Travel Behavior Comprehension","year":2010,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Human Mobility and Location-Based Analysis","field":"Social Sciences","cited_by":80,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Computer science; Travel behavior; Smart card; Schedule; Transport engineering; Dimension (graph theory); Data collection; TRIPS architecture; Public transport; Transit (satellite); Mode choice; Trip generation; Operations research; Data science; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001388947,0.0008504142,0.0006423178,0.00262461,0.0002840095,0.00184058,0.0007294672,0.000650133,0.004357203],"category_scores_gemma":[0.01111238,0.0002806933,0.0005564592,0.002047024,0.0003913124,0.0047885,0.001468183,0.000697318,0.001157931],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004520811,"about_ca_system_score_gemma":0.000640919,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004006503,"about_ca_topic_score_gemma":0.003172841,"domain_scores_codex":[0.9987878,0.0005337492,0.00007769595,0.000376925,0.0001661234,0.00005769579],"domain_scores_gemma":[0.992971,0.003928235,0.0006721446,0.001194984,0.001116774,0.0001169312],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0008535046,0.0008439525,0.06918285,0.0009147738,0.0002692685,0.000384756,0.008658229,0.03373403,0.04624068,0.01374132,0.007074219,0.8181024],"study_design_scores_gemma":[0.00004200833,0.000817335,0.08805227,0.0001657895,0.0002533753,0.000810698,0.007312659,0.7968149,0.04201734,0.02401048,0.03953554,0.0001675878],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2799706,0.0003214372,0.6960405,0.0007057137,0.00003243944,0.0004195587,0.004401849,0.00720886,0.01089901],"genre_scores_gemma":[0.7924704,0.0002176663,0.1993288,0.00007756719,0.00004485239,0.0002220993,0.005294014,0.0003292809,0.002015334],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004357203,"threshold_uncertainty_score":0.01457626,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08745452729732066,"score_gpt":0.4036590065868538,"score_spread":0.3162044792895332,"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."}}