{"id":"W2012136844","doi":"10.1139/l04-073","title":"Construction prequalification using data envelopment analysis","year":2005,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Efficiency Analysis Using DEA","field":"Decision Sciences","cited_by":64,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Data envelopment analysis; Envelopment; Process (computing); Engineering; Key (lock); Operations management; Computer science; Operations research; Mathematics; Statistics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"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":[0.00364798,0.0001135689,0.0003282287,0.002507317,0.0001311176,0.0002798247,0.001097845,0.00005749455,0.0004649067],"category_scores_gemma":[0.002132068,0.0001023229,0.000145631,0.002699481,0.00005102257,0.0007798754,0.00003105394,0.0001744929,0.00001985303],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000318066,"about_ca_system_score_gemma":0.0008352178,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005000752,"about_ca_topic_score_gemma":0.0838288,"domain_scores_codex":[0.9976485,0.00006860875,0.0009871053,0.0002531091,0.0007822166,0.000260417],"domain_scores_gemma":[0.9976967,0.0002199376,0.0004951833,0.0007199184,0.0004393615,0.0004288793],"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":[0.000001183651,0.000004750572,0.006582358,0.000001330452,0.0002009512,0.000009579881,0.0003034383,0.9827651,0.0005318234,0.0002717505,0.0004629065,0.008864861],"study_design_scores_gemma":[0.0001151836,0.000008869282,0.007668149,0.00002423622,0.0004034992,0.00009955373,0.0002113126,0.9469485,0.0002273603,0.0001314757,0.04399173,0.0001701567],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1755205,0.0007590509,0.8218517,0.0006745607,0.0005372685,0.00004775605,0.00002510043,0.00001075922,0.0005732414],"genre_scores_gemma":[0.978205,0.000007292639,0.02148369,0.00003712723,0.0002121281,1.916385e-7,0.000005994793,0.000008449147,0.00004010745],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8026845,"threshold_uncertainty_score":0.9328889,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1030891430529928,"score_gpt":0.3335333906077694,"score_spread":0.2304442475547767,"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."}}