{"id":"W2361456725","doi":"","title":"Study on Cultivation Techniques for High Yield of Rice","year":2015,"lang":"en","type":"article","venue":"Horticulture & Seed","topic":"Rice Cultivation and Yield Improvement","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Sowing; Yield (engineering); Quarter (Canadian coin); China; Agronomy; Production (economics); Agricultural economics; Agricultural science; Agricultural engineering; Agroforestry; Environmental science; Geography; Engineering; Biology; Economics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.000413153,0.0004022348,0.0002605541,0.0007619475,0.0003733079,0.0004051953,0.0003456588,0.0002149234,0.0008187719],"category_scores_gemma":[0.0003843186,0.0001596406,0.0005860715,0.0009545407,0.0002257865,0.0007706129,0.0002919147,0.0005660877,0.0004149042],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002766734,"about_ca_system_score_gemma":0.0004269984,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000928322,"about_ca_topic_score_gemma":0.0009328924,"domain_scores_codex":[0.9997202,0.00004264276,0.00003037591,0.00006915394,0.0001041373,0.00003353046],"domain_scores_gemma":[0.9998578,0.00003516887,0.00003251422,0.00001607768,0.00004718968,0.00001121283],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00005785086,0.00009208718,0.001123529,0.0006409668,0.00001241486,0.0001525185,0.000164892,0.0002703433,0.9426677,0.0005943002,0.0002055079,0.05401801],"study_design_scores_gemma":[0.00002137107,0.001664003,0.02399625,0.000158414,0.0001571206,0.001368479,0.0004155902,0.001951251,0.9209439,0.0008144886,0.04845126,0.00005785232],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7758398,0.0627481,0.1397605,0.000773341,0.0004158459,0.0003053595,0.0005290666,0.0003441537,0.01928389],"genre_scores_gemma":[0.7960932,0.07613294,0.1147376,0.0002968773,0.0001882523,0.0002336131,0.001072009,0.0001781852,0.01106722],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.000928322,"threshold_uncertainty_score":0.002739012,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06665928608741789,"score_gpt":0.2774270426274724,"score_spread":0.2107677565400545,"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."}}