{"id":"W3206191070","doi":"10.3390/jrfm14100498","title":"An Assessment of the Efficiency of Canadian Power Generation Companies with Bootstrap DEA","year":2021,"lang":"en","type":"article","venue":"Journal of risk and financial management","topic":"Efficiency Analysis Using DEA","field":"Decision Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Laurentian University","funders":"","keywords":"Electricity generation; Electricity; Benchmark (surveying); Business; Scale (ratio); Sample (material); Power (physics); Environmental economics; Data envelopment analysis; Service (business); Electric power industry; Industrial organization; Economics; Marketing; Statistics; Engineering; Mathematics; Geography","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.005751435,0.0004501853,0.0005786705,0.002562767,0.0007224511,0.001349791,0.0008620124,0.0003009619,0.0009449683],"category_scores_gemma":[0.02742461,0.0001718861,0.0005306941,0.004356233,0.0005910455,0.0007055844,0.0006749427,0.0005336089,0.0001336757],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.008456288,"about_ca_system_score_gemma":0.006839387,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.7071745,"about_ca_topic_score_gemma":0.7091718,"domain_scores_codex":[0.9968141,0.000650671,0.0001453356,0.000294437,0.001653309,0.0004422535],"domain_scores_gemma":[0.9842533,0.007474006,0.001312749,0.001319729,0.005430549,0.0002096251],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006612762,0.0002807188,0.4246713,0.0002705794,0.0005537287,0.0003235256,0.001086986,0.362618,0.0029089,0.03007732,0.004602883,0.1719448],"study_design_scores_gemma":[0.00005366164,0.0002008629,0.5068682,0.00005446024,0.0001421764,0.0001076716,0.001717867,0.4728489,0.003762851,0.005147866,0.009015567,0.00007973561],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9653907,0.0004488579,0.02055882,0.000209047,0.000008646703,0.0001143852,0.001433742,0.00006887922,0.0117669],"genre_scores_gemma":[0.9888719,0.0001925797,0.00842075,0.00001985592,0.000004676307,0.00003652589,0.001492526,0.00001242477,0.0009488013],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2928255,"threshold_uncertainty_score":0.5891,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03776791021737175,"score_gpt":0.3355404893735144,"score_spread":0.2977725791561427,"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."}}