{"id":"W2354382166","doi":"","title":"Analysis on High Yield of Corn Seed Production in Xinjiang Yili","year":2011,"lang":"en","type":"article","venue":"Seed","topic":"Medical Research and Treatments","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Yield (engineering); Agronomy; Production (economics); Biology; Agroforestry; Economics; Materials science","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002421301,0.0002166298,0.0001796804,0.0007758504,0.0004568569,0.0002642487,0.0001645219,0.0001173627,0.0006871298],"category_scores_gemma":[0.0001600465,0.0001151165,0.0002721157,0.0008765946,0.0001543622,0.0001075179,0.0001681458,0.0001163552,0.00008563117],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000449315,"about_ca_system_score_gemma":0.0004243285,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008510635,"about_ca_topic_score_gemma":0.01573398,"domain_scores_codex":[0.9999101,0.00001114162,0.000008622784,0.00002668208,0.00002031568,0.00002321035],"domain_scores_gemma":[0.9998411,0.0000409491,0.00004505385,0.000008718833,0.00003056225,0.0000336735],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000708485,0.0001254008,0.807149,0.0000810982,0.0001213401,0.001181389,0.000714722,0.0008144882,0.1781244,0.0002727338,0.000191928,0.010515],"study_design_scores_gemma":[0.000004133728,0.00007819338,0.9938071,0.000001701159,0.00003922788,0.0001170685,0.0002993243,0.0004984098,0.004794715,0.00003046682,0.0003262781,0.000003282981],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.999506,0.00004541174,0.0001206302,0.000007684725,7.697249e-7,0.000002293538,0.0001243796,0.000002713357,0.0001901233],"genre_scores_gemma":[0.9990115,0.00005340462,0.0001631432,0.000007045563,0.000001268229,0.000003116631,0.0003562603,0.000002251081,0.0004019927],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008510635,"threshold_uncertainty_score":0.01692224,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05934179703554359,"score_gpt":0.3135468092970876,"score_spread":0.254205012261544,"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."}}