{"id":"W3114127759","doi":"10.3390/su13010097","title":"Analysis on the Agricultural Green Production Efficiency and Driving Factors of Urban Agglomerations in the Middle Reaches of the Yangtze River","year":2020,"lang":"en","type":"article","venue":"Sustainability","topic":"Land Use and Ecosystem Services","field":"Environmental Science","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Fundamental Research Funds for the Central Universities; Ministry of Education of the People's Republic of China; National Natural Science Foundation of China","keywords":"Urban agglomeration; Agriculture; Tobit model; Agricultural productivity; Production (economics); Economies of agglomeration; China; Yangtze river; Panel data; Environmental science; Agricultural economics; Natural resource economics; Economic geography; Geography; Economics; Economic growth","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":[0.0005765252,0.000280589,0.0002490824,0.001404463,0.0004381901,0.0009485765,0.0003542183,0.0002594025,0.001622655],"category_scores_gemma":[0.001267594,0.000196002,0.000802165,0.002488354,0.0004482618,0.0007287272,0.0009454673,0.0003263088,0.0001455777],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009533686,"about_ca_system_score_gemma":0.001012039,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04193946,"about_ca_topic_score_gemma":0.04327334,"domain_scores_codex":[0.9995198,0.0001044298,0.00003941501,0.0001190766,0.00009720131,0.0001199809],"domain_scores_gemma":[0.9992906,0.0001936442,0.0001455207,0.00006260637,0.0001971391,0.0001103854],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001919234,0.00002613543,0.9888567,0.00003305306,0.0001141604,0.0003199331,0.0009205684,0.003892891,0.0004792474,0.001094166,0.0003819316,0.003861975],"study_design_scores_gemma":[0.000002333389,0.00001147112,0.989646,0.00001168015,0.00003635971,0.00004032752,0.002379712,0.006560655,0.0001495698,0.0003094397,0.0008421898,0.00001022126],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9979998,0.00007134681,0.000472051,0.000099819,0.000002278415,0.00001002712,0.0002708362,0.00001375489,0.001060002],"genre_scores_gemma":[0.9994494,0.0000340133,0.0001024451,0.000006634117,0.000001695926,0.000008200639,0.000226884,0.00000299158,0.0001677931],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04193946,"threshold_uncertainty_score":0.08339071,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01458645595444124,"score_gpt":0.2001375353493602,"score_spread":0.185551079394919,"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."}}