{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","bibliometrics","open_science","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.1038742,0.0003450206,0.001348677,0.005218843,0.0008667456,0.0003733015,0.00672908,0.0003212619,0.0002709913],"category_scores_gemma":[0.002566777,0.0002314995,0.0009384367,0.02104549,0.001143048,0.00099522,0.00004260283,0.002547515,0.00002641893],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002737667,"about_ca_system_score_gemma":0.0009911171,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.01091294,"about_ca_topic_score_gemma":0.05232972,"domain_scores_codex":[0.9694616,0.004296162,0.005675791,0.001204804,0.0180087,0.00135293],"domain_scores_gemma":[0.9752249,0.0109842,0.001896004,0.002423121,0.008887165,0.0005846117],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002802724,0.001800943,0.8061829,0.0002588572,0.001984477,0.0002577359,0.005339072,0.05825248,0.07306115,0.0045903,0.02085025,0.0246191],"study_design_scores_gemma":[0.001362615,0.0006092592,0.9486337,0.0002654456,0.0008463088,0.000001930762,0.007116961,0.01064516,0.008736145,0.001058292,0.02028915,0.0004350661],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5957296,0.0007051553,0.4009151,0.001252489,0.0003605446,0.0006641371,0.0001985754,0.00001906431,0.0001553572],"genre_scores_gemma":[0.9846364,0.0003991138,0.01434202,0.00002164498,0.00008446057,0.00001212475,0.00005805471,0.00003653349,0.0004096005],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3889068,"threshold_uncertainty_score":0.9997627,"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."}}