{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003858664,0.0004220347,0.000482389,0.00057541,0.0002122026,0.0003360988,0.0005506998,0.0002621553,0.0008469958],"category_scores_gemma":[0.0002890104,0.0003653787,0.0003802626,0.0005047943,0.0001900469,0.000130947,0.000355434,0.0007500809,0.00023278],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003532021,"about_ca_system_score_gemma":0.0002655982,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001248241,"about_ca_topic_score_gemma":0.003075865,"domain_scores_codex":[0.9998441,0.00002952288,0.00001783644,0.00004684431,0.00003454234,0.00002710918],"domain_scores_gemma":[0.9997172,0.00008610048,0.00007877145,0.00003510133,0.00002161174,0.00006128551],"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":[0.0001793955,0.00002670802,0.0008579175,0.00001851719,0.00001784528,0.0001077901,0.00005600081,0.0002251096,0.9947701,0.00008726859,0.00003596947,0.003617405],"study_design_scores_gemma":[0.0001521716,0.0007043502,0.07893629,0.0000172908,0.0002747822,0.001099225,0.0001926554,0.008741297,0.9024876,0.0002812108,0.007043735,0.00006937484],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9913059,0.0001867677,0.007460956,0.00005011013,0.00001576478,0.00002435256,0.0003011655,0.000214917,0.0004401612],"genre_scores_gemma":[0.9884602,0.0002016422,0.008342829,0.00004721337,0.000008019245,0.000030613,0.0008558668,0.000155396,0.001898254],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001248241,"threshold_uncertainty_score":0.002833426,"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."}}