{"id":"W2139291664","doi":"10.3141/2034-13","title":"Quantifying Technical Efficiency of Paratransit Systems by Data Envelopment Analysis Method","year":2007,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Efficiency Analysis Using DEA","field":"Decision Sciences","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Paratransit; Data envelopment analysis; Identification (biology); Regression analysis; Computer science; Operations research; Quality (philosophy); Level of service; Performance measurement; Resource allocation; Transport engineering; Efficiency; Engineering; Public transport; Statistics; Business; Mathematics; Marketing","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.003353319,0.0006094809,0.0008051433,0.003764612,0.0005830114,0.00170101,0.0006212646,0.0003887583,0.001156593],"category_scores_gemma":[0.01339341,0.0002010429,0.0008917323,0.008322132,0.0005909636,0.001249986,0.0007769894,0.0005288276,0.0001934113],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004597066,"about_ca_system_score_gemma":0.00535723,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.09000129,"about_ca_topic_score_gemma":0.05608627,"domain_scores_codex":[0.9963602,0.0007807832,0.000246955,0.0002849138,0.002042606,0.0002845401],"domain_scores_gemma":[0.9952044,0.00262937,0.0005361794,0.0004407586,0.001148213,0.00004108962],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0001775289,0.0001801168,0.05628531,0.0004287667,0.0002621796,0.0001145535,0.0005113532,0.7366792,0.004164131,0.04143429,0.001076051,0.1586865],"study_design_scores_gemma":[0.00002304715,0.0002279971,0.05246378,0.0001144317,0.0001081305,0.0000700233,0.0006864775,0.9114681,0.01059739,0.01454112,0.009621519,0.00007784898],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4055438,0.0006514743,0.5756819,0.0001376761,0.0000151104,0.0004085328,0.00266358,0.0002516485,0.01464623],"genre_scores_gemma":[0.890778,0.0004848702,0.1056105,0.00001406357,0.000004537279,0.0003853656,0.001378722,0.00003031962,0.001313689],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.09000129,"threshold_uncertainty_score":0.1789548,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3796476953601691,"score_gpt":0.5442297430945094,"score_spread":0.1645820477343404,"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."}}