{"id":"W4285390396","doi":"10.1007/s11769-022-1294-0","title":"Coupling Relationship Among Technological Innovation, Industrial Transformation and Environmental Efficiency: A Case Study of the Huaihai Economic Zone, China","year":2022,"lang":"en","type":"article","venue":"Chinese Geographical Science","topic":"Energy, Environment, Economic Growth","field":"Economics, Econometrics and Finance","cited_by":17,"is_retracted":false,"has_abstract":false,"ca_institutions":"Innovation Cluster (Canada)","funders":"National Social Science Fund of China; Government of Jiangsu Province","keywords":"China; Exclusive economic zone; Coupling (piping); Transformation (genetics); Economic geography; Economic system; Chemistry; Geography; Engineering; Economics; Political science; Mechanical engineering","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021777,0.0001606322,0.0002591533,0.0006249718,0.001283255,0.00005841758,0.0005403199,0.00008349347,0.0001007405],"category_scores_gemma":[0.0001482694,0.0001407526,0.00006196506,0.001792749,0.001365122,0.0004085482,0.0004230591,0.0004257846,0.000002868731],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002051915,"about_ca_system_score_gemma":0.00002595571,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004456575,"about_ca_topic_score_gemma":0.00008717311,"domain_scores_codex":[0.9982037,0.00003135782,0.0008182916,0.0005598074,0.0001188856,0.0002679811],"domain_scores_gemma":[0.9989656,0.00007681234,0.0004865344,0.0004052884,0.000004088768,0.00006166814],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00000604818,0.0002277469,0.940828,0.000001849887,0.000007551277,0.000003366181,0.0007212003,0.02065617,0.00004824503,0.03737139,0.000001549394,0.0001268409],"study_design_scores_gemma":[0.0009084536,0.0001486454,0.9397861,0.000002063206,0.000006454011,0.00008450932,0.001836356,0.04818015,0.00001492659,0.008777821,0.00005562719,0.0001989433],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9982191,0.00005732938,0.0002524565,0.0003340711,0.0002124561,0.0005462632,0.00007013389,0.0000269607,0.0002811671],"genre_scores_gemma":[0.9998141,0.000007679798,0.00002274517,0.00002030032,0.00002040549,0.00008734459,0.000006672333,0.000009726707,0.00001106222],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02859356,"threshold_uncertainty_score":0.9869888,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01904740786452014,"score_gpt":0.2076985140413356,"score_spread":0.1886511061768155,"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."}}