{"id":"W921572213","doi":"","title":"Marker Assisted Selection for Submergence Tolerance in Rice","year":2007,"lang":"en","type":"article","venue":"分子植物育种","topic":"Plant responses to water stress","field":"Agricultural and Biological Sciences","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Selection (genetic algorithm); Biology; Computer science; Artificial intelligence","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":[0.0003931379,0.00007143609,0.00008196122,0.00001343173,0.00006357938,0.00001842928,0.000117217,0.00006105491,0.00004904809],"category_scores_gemma":[0.00004585392,0.00002872896,0.00003454291,0.0003279801,0.00001284601,0.0000749343,0.00001486405,0.00005565314,0.00001357086],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002704621,"about_ca_system_score_gemma":0.00000312927,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005009455,"about_ca_topic_score_gemma":0.01420603,"domain_scores_codex":[0.9992841,0.00003710882,0.0001374652,0.0001831333,0.0000898742,0.0002682791],"domain_scores_gemma":[0.9996889,0.0001809021,0.00003695604,0.00002216083,0.00003140609,0.0000397397],"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":[0.0002638753,0.0000546489,0.0751593,0.000006416798,0.000002988706,0.000005697353,0.00002356902,0.00000604481,0.8044972,0.00003160158,0.001156576,0.1187921],"study_design_scores_gemma":[0.00009003313,0.00004533504,0.957668,0.000009166118,0.000001654732,0.00001050307,0.00001455358,0.0001523087,0.01914328,0.00004906999,0.02272663,0.00008943061],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9975792,0.00004804628,0.00009375141,0.0002555193,0.0001639017,0.0001645529,0.00001908707,0.00004019298,0.001635802],"genre_scores_gemma":[0.9982362,0.000007995233,0.0002755178,0.00008264453,0.0001617824,0.00001849262,0.00002244919,4.626037e-7,0.001194492],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8825088,"threshold_uncertainty_score":0.7927297,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02223480521222162,"score_gpt":0.2404407508255557,"score_spread":0.2182059456133341,"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."}}