{"id":"W2018829876","doi":"10.3141/1760-12","title":"Simulation Model for Evaluating Intelligent Paratransit Systems","year":2001,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation and Mobility Innovations","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Paratransit; Field (mathematics); Computer science; Reliability (semiconductor); Transport engineering; Variety (cybernetics); TRIPS architecture; Intelligent transportation system; Public transport; Systems engineering; Risk analysis (engineering); Engineering; Simulation; Operations research","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.004703139,0.0002998055,0.0004721256,0.001040503,0.0006419753,0.0001696349,0.0007732183,0.0002428409,0.00009954518],"category_scores_gemma":[0.0002006305,0.0002523065,0.0004331347,0.002276309,0.0003007992,0.000726965,0.000002127552,0.001571564,0.00001471625],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003284229,"about_ca_system_score_gemma":0.0004383596,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007944731,"about_ca_topic_score_gemma":0.01061937,"domain_scores_codex":[0.9935021,0.0003975033,0.002030685,0.0003637807,0.002814553,0.0008913474],"domain_scores_gemma":[0.9915639,0.001495645,0.0002836064,0.0004854073,0.005857396,0.0003140716],"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":[0.0005489426,0.0001219569,0.01268063,0.0003055443,0.0001478449,0.00001029762,0.002770112,0.9713531,0.002856486,0.003777314,0.001294918,0.004132903],"study_design_scores_gemma":[0.001556062,0.0003911831,0.1017284,0.000298421,0.0001078601,4.649041e-7,0.001766438,0.8790644,0.0005256882,0.002903635,0.01135632,0.000301131],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.632612,0.0002514977,0.3638969,0.0005808448,0.0006881236,0.001675012,0.0001171272,0.00008504949,0.00009332036],"genre_scores_gemma":[0.9941868,0.0005914203,0.003960154,0.00002985426,0.0001934379,0.0002957633,0.0000676065,0.0000908949,0.0005841147],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3615747,"threshold_uncertainty_score":0.9999929,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.264538604329994,"score_gpt":0.4540707059312021,"score_spread":0.1895321016012081,"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."}}