{"id":"W2792202250","doi":"10.4236/ti.2018.91004","title":"Study on the Efficiency and Total Factor Productivity of China’s Securities Companies—Based on Hicks-Moorsteen TFP Index Method","year":2018,"lang":"en","type":"article","venue":"Technology and Investment","topic":"Efficiency Analysis Using DEA","field":"Decision Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Total factor productivity; Index (typography); Security market; Sample (material); Scope (computer science); China; Business; Productivity; Scale (ratio); Capital (architecture); Economics; Industrial organization; Finance; Monetary economics; Macroeconomics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002072494,0.0004423243,0.0004580168,0.004364305,0.0002670588,0.00102567,0.0003416771,0.0002667745,0.0008809415],"category_scores_gemma":[0.004346929,0.0001550047,0.001029311,0.003417067,0.0005071196,0.001100565,0.0003442956,0.0003623356,0.0001445239],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001253554,"about_ca_system_score_gemma":0.0009202132,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01534773,"about_ca_topic_score_gemma":0.007861495,"domain_scores_codex":[0.9991121,0.0001556234,0.00009361401,0.0001977247,0.0002989903,0.0001419638],"domain_scores_gemma":[0.997772,0.001312041,0.0003480753,0.0001580157,0.0003447051,0.00006520611],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001414869,0.0001356943,0.8058307,0.000230595,0.0004896957,0.0009200867,0.001234948,0.06068296,0.005646243,0.01599973,0.00173617,0.1069517],"study_design_scores_gemma":[0.00001626757,0.0001123597,0.8471945,0.00004675404,0.0001371823,0.0001983485,0.000811429,0.1415585,0.003587016,0.003649004,0.002647399,0.00004120171],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.977463,0.0006127348,0.01777855,0.0001363307,0.00001138347,0.00003147812,0.0004207192,0.00002443436,0.003521302],"genre_scores_gemma":[0.9964489,0.0002288432,0.002084396,0.000007194663,0.00001264282,0.00001408421,0.0004509647,0.000005845188,0.0007472007],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01534773,"threshold_uncertainty_score":0.03051674,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0641278728468186,"score_gpt":0.3665742899713185,"score_spread":0.3024464171244999,"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."}}