{"id":"W2045818236","doi":"10.3926/jiem.1275","title":"Research on the Competitiveness of Crediting Rating Industry using PCA Method","year":2014,"lang":"en","type":"article","venue":"Journal of Industrial Engineering and Management","topic":"Efficiency Analysis Using DEA","field":"Decision Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Fundamental Research Funds for the Central Universities","keywords":"Lagging; Credit rating; Rating system; Competition (biology); Actuarial science; Originality; Business; Empirical research; Accounting; Economics; Environmental economics; Statistics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.03144605,0.0001079462,0.0003453575,0.0008532744,0.0001934974,0.0001761843,0.0005381619,0.0001250895,0.0000330743],"category_scores_gemma":[0.005837617,0.00006673026,0.0001027253,0.001293473,0.0000718832,0.0001208195,0.00018563,0.001017681,0.000001522091],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005663346,"about_ca_system_score_gemma":0.00004170286,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001513393,"about_ca_topic_score_gemma":4.775326e-7,"domain_scores_codex":[0.9962754,0.0007429462,0.0008691737,0.0001820157,0.001704335,0.0002260797],"domain_scores_gemma":[0.9936101,0.004992693,0.0005735862,0.0002774761,0.0004658141,0.00008036732],"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":[0.00004299993,0.00005031064,0.0007427434,0.00001748199,0.0001230908,0.0000154072,0.0001987685,0.9348083,0.001859205,0.02771551,0.0003847761,0.03404142],"study_design_scores_gemma":[0.001703185,0.0005824426,0.00220446,0.002186965,0.0001825045,0.00005391855,0.01030458,0.9498321,0.008851442,0.001447916,0.0223321,0.0003183312],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8515001,0.0000577374,0.1442873,0.001175428,0.0007167382,0.0001462919,0.000001396117,0.000008047286,0.002106944],"genre_scores_gemma":[0.9934631,0.000006943873,0.00585945,0.0000340032,0.0005562659,0.000001010037,8.696731e-8,0.00000892719,0.00007019321],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.141963,"threshold_uncertainty_score":0.9973301,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2881172524778674,"score_gpt":0.4474281160766733,"score_spread":0.1593108635988059,"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."}}