{"id":"W2352682145","doi":"","title":"Guizhou Tea Tree Species Resources and Tea Industrial Economic Development Analysis","year":2009,"lang":"en","type":"article","venue":"Seed","topic":"Wine Industry and Tourism","field":"Business, Management and Accounting","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Tree (set theory); Economic analysis; Chemistry; Business; Agricultural economics; Economics; Mathematics","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":[0.000229346,0.000158106,0.0002407547,0.0003519715,0.0001912759,0.0003024487,0.0001628363,0.0001217835,0.0002520039],"category_scores_gemma":[0.00003555392,0.0001432539,0.00006786383,0.0003521354,0.00003548815,0.0003999491,0.00007657723,0.0001536166,0.0001499195],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003368468,"about_ca_system_score_gemma":0.00002198144,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001935366,"about_ca_topic_score_gemma":0.00007899165,"domain_scores_codex":[0.9991336,0.000007264501,0.000261668,0.00025389,0.0001168941,0.0002267224],"domain_scores_gemma":[0.9996388,0.00001910259,0.0001387669,0.0001628001,0.00002186651,0.00001868145],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001851217,0.0001795006,0.8557205,0.00002389557,0.0009176681,0.00005810637,0.0005791036,0.0004645116,0.0005455114,0.002322457,0.09934841,0.03965518],"study_design_scores_gemma":[0.0004763744,0.000005729004,0.6245427,0.000008254543,0.0001580223,5.554406e-7,0.0001756854,0.0002352786,0.00009781559,0.0002330484,0.3738985,0.0001680738],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9303867,0.00002307597,0.00001018458,0.004439694,0.000146305,0.00009318072,0.000001408382,0.00008230179,0.06481718],"genre_scores_gemma":[0.9866926,0.000001120562,0.00009401601,0.0005465045,0.006736489,0.000002785575,0.00002723672,0.000008609314,0.005890616],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2745501,"threshold_uncertainty_score":0.5841725,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02590653983215753,"score_gpt":0.2078319552625742,"score_spread":0.1819254154304167,"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."}}