{"id":"W1554315107","doi":"10.1108/nbri-01-2015-0003","title":"Effects of technological innovation on eco-efficiency of industrial enterprises in China","year":2015,"lang":"en","type":"article","venue":"Nankai Business Review International","topic":"Energy, Environment, Economic Growth","field":"Economics, Econometrics and Finance","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Originality; China; Business; Industrial organization; Technology transfer; Technological change; Value (mathematics); Mode (computer interface); Economic system; Economic geography; Economics; International trade; Geography; Political science; Computer science","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"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":[0.001407452,0.0003154938,0.0002959193,0.001464418,0.000396054,0.00103391,0.0003103743,0.0003009493,0.001168477],"category_scores_gemma":[0.002515611,0.0001321589,0.0006817793,0.001570607,0.0005917377,0.0008484142,0.001105917,0.0003326848,0.0001210082],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001898897,"about_ca_system_score_gemma":0.00220362,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01653975,"about_ca_topic_score_gemma":0.01827815,"domain_scores_codex":[0.9990946,0.0002442336,0.00006292634,0.0001187264,0.0002139335,0.0002656704],"domain_scores_gemma":[0.9968252,0.001012574,0.001081345,0.0001754891,0.0005242187,0.0003811701],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00009786613,0.0001434994,0.964984,0.0000893821,0.0001657538,0.0004120357,0.0005061364,0.01118101,0.001273225,0.001703068,0.0002120361,0.019232],"study_design_scores_gemma":[0.000007805677,0.0001050924,0.9931893,0.00001887302,0.00005147966,0.00005168045,0.0004502363,0.004607859,0.0005357024,0.0004414214,0.0005317228,0.000008825829],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.99717,0.000354909,0.0002632542,0.0001086939,0.000002279592,0.000008058229,0.00004577151,0.000007354889,0.002039653],"genre_scores_gemma":[0.9995613,0.0001269011,0.00006382614,0.000007004734,0.000001842625,0.00000254596,0.00002803,6.818384e-7,0.0002079009],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01653975,"threshold_uncertainty_score":0.03288698,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03944510238300375,"score_gpt":0.2466093991115768,"score_spread":0.2071642967285731,"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."}}