{"id":"W1640093880","doi":"","title":"Obstacles and Countermeasures For Building Unified Urban and Rural Construction Land Market","year":2015,"lang":"en","type":"article","venue":"Cross-cultural communication","topic":"Regional Economic and Spatial Analysis","field":"Economics, Econometrics and Finance","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Business; Environmental planning; Resource (disambiguation); Land use; Natural resource economics; Logical analysis; Land price; Environmental resource management; Geography; Economics; Civil engineering; Agricultural economics; Computer science; Engineering","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004087993,0.0001005053,0.0002235503,0.00005457953,0.0002651016,0.0003646044,0.0001390151,0.00007107868,0.00001225342],"category_scores_gemma":[0.0001461203,0.00009680764,0.00004678755,0.00005335583,0.0003433611,0.0005644233,0.00006489744,0.00006901724,0.000004019354],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006772084,"about_ca_system_score_gemma":0.000009025598,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005488671,"about_ca_topic_score_gemma":0.0000836163,"domain_scores_codex":[0.9993601,0.00001816379,0.0003157213,0.0001687889,0.00002107041,0.0001160949],"domain_scores_gemma":[0.9992827,0.00007132076,0.0002306823,0.0002151296,0.000127393,0.00007283511],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001606446,0.00002147311,0.728866,0.00003820185,0.0001119753,1.547094e-7,0.0008832022,0.0001091685,0.00008095638,0.262093,0.003097828,0.004537313],"study_design_scores_gemma":[0.003942146,0.0001524542,0.4684212,0.00007367805,0.00005911493,0.00003738008,0.002587259,0.054167,0.0001265006,0.2360788,0.2335274,0.0008270805],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9902167,0.004470906,0.0004395633,0.00080497,0.00007165267,0.0001434058,0.0001046664,0.0000252165,0.00372295],"genre_scores_gemma":[0.9967697,0.0006821239,0.001868901,0.00007160358,0.00004842429,0.0000215473,0.00009150485,0.000008028005,0.0004381824],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2604448,"threshold_uncertainty_score":0.3947701,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04878311666979034,"score_gpt":0.2782463493431751,"score_spread":0.2294632326733848,"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."}}