{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001850574,0.0001088251,0.0001461596,0.000009421866,0.00006424905,0.00002405268,0.0001188285,0.00006909935,0.00001677086],"category_scores_gemma":[0.0002282731,0.00003644521,0.00004836916,0.0002139361,0.00001490266,0.00008097947,0.00002173423,0.00005894408,0.000005330017],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002664704,"about_ca_system_score_gemma":0.000005251509,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001949627,"about_ca_topic_score_gemma":0.00006852116,"domain_scores_codex":[0.9991993,0.00002624082,0.0002316999,0.0001941899,0.000219501,0.0001291235],"domain_scores_gemma":[0.9993936,0.00008718236,0.0001296017,0.00005450605,0.0002715124,0.00006361889],"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":[0.00009225099,0.0007079571,0.004955207,0.000006128074,0.00002122019,5.265244e-7,0.0009608623,0.000001716291,0.9831126,0.0005819497,0.004535333,0.005024246],"study_design_scores_gemma":[0.0004060937,0.003134861,0.6545212,0.00002424939,0.00002715596,5.677494e-7,0.00892304,0.000005417064,0.3266802,0.0002622126,0.00583304,0.0001818666],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9963136,0.000008450877,0.00005211309,0.001307392,0.00008936253,0.0009900928,0.00001560846,0.00008090113,0.001142441],"genre_scores_gemma":[0.9984507,0.000001258722,0.0002433004,0.0003943763,0.0001626766,0.0001172876,0.00005150806,8.616424e-7,0.0005780153],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6564323,"threshold_uncertainty_score":0.1486193,"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."}}