{"id":"W2357560905","doi":"","title":"Performance and High-yielding Cultural Techniques of High Quality Rice Variety Zhongguangxiang No.1 with Coldness-resistant and High Iron Content and Scent","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":"Variety (cybernetics); Quality (philosophy); Horticulture; Biology; Computer science; Artificial intelligence; Physics","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":[0.0002667574,0.0001464167,0.0003397015,0.00004224374,0.00008773139,0.00001468333,0.00004226619,0.0000755058,0.00002580631],"category_scores_gemma":[0.0001458308,0.00008744897,0.00001682706,0.00008149183,0.0003025686,0.0001085625,0.00006585765,0.0001476944,0.000001529748],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004229547,"about_ca_system_score_gemma":0.00004682175,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004887354,"about_ca_topic_score_gemma":0.00006946011,"domain_scores_codex":[0.9988977,0.00004672792,0.0002145042,0.0002495724,0.0003446417,0.0002468561],"domain_scores_gemma":[0.9992171,0.00007076842,0.00008978674,0.0001578423,0.0001733463,0.0002911723],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.008220013,0.0007192053,0.8640542,0.002500304,0.000399202,0.0001632893,0.002063813,3.30528e-8,0.108871,0.001951587,0.0001481282,0.01090924],"study_design_scores_gemma":[0.002597897,0.001658068,0.9660425,0.0004114109,0.0000897799,0.00001649785,0.000309793,0.00002282418,0.02866713,0.0000480626,0.00003110096,0.0001049514],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9984368,0.0002416868,0.00005169259,0.0002124344,0.00002903777,0.0004655063,0.00001656826,0.00003415555,0.000512129],"genre_scores_gemma":[0.9952851,0.0005706883,0.003633554,0.0000855225,0.00003039698,0.0000254108,0.00001867858,0.0000102998,0.0003403654],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1019883,"threshold_uncertainty_score":0.738825,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0620307307066098,"score_gpt":0.3040709524198303,"score_spread":0.2420402217132205,"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."}}