{"id":"W2384367607","doi":"","title":"Study on Policy Development,Practical Benefits and Improvement Countermeasures of Rural Land Circulation","year":2015,"lang":"en","type":"article","venue":"Anhui nongye kexue","topic":"Environmental and Agricultural Sciences","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Circulation (fluid dynamics); Promotion (chess); China; Business; Government (linguistics); Service (business); Land management; Economic growth; Rural area; Agriculture; Natural resource economics; Economics; Geography; Political 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002534654,0.0001151212,0.0001088665,0.00002240818,0.0000968207,0.00002921179,0.00009783155,0.00003122469,0.0000534053],"category_scores_gemma":[0.00002976378,0.00007352172,0.00001569044,0.0001398249,0.0001168505,0.0002614813,0.0001427348,0.00005663924,0.00008822662],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001219525,"about_ca_system_score_gemma":0.00001433493,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009208585,"about_ca_topic_score_gemma":0.000385487,"domain_scores_codex":[0.9989274,0.00002951874,0.0001759597,0.0001831838,0.0005051788,0.0001787888],"domain_scores_gemma":[0.9996602,0.00002364912,0.00007841094,0.00009258003,0.000006228016,0.0001389322],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002019063,0.0003207223,0.9627532,0.000001932136,0.00000955132,0.000003025922,0.001635819,0.0002944994,0.008256581,0.00003989157,0.0001274987,0.02653713],"study_design_scores_gemma":[0.0003979368,0.0003969661,0.9931368,0.00001067589,0.00000644555,0.000006927716,0.002187417,0.00002052292,0.003289828,0.00005911084,0.0003660552,0.0001213272],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9969071,0.00001687593,0.000003667329,0.0003131904,0.00004556664,0.0002346913,0.000002885683,0.00001111388,0.002464959],"genre_scores_gemma":[0.9996063,0.000008558201,0.0001006194,0.0001185644,0.00003688382,0.00001140949,0.000004033932,0.0000035626,0.0001100892],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03038363,"threshold_uncertainty_score":0.2998129,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03456211058364003,"score_gpt":0.2661910631861459,"score_spread":0.2316289526025058,"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."}}