{"id":"W380266657","doi":"","title":"Genome Maps, Genetic Diversity and Marker-Assisted Selection for Soybean Improvement","year":2007,"lang":"en","type":"article","venue":"分子植物育种","topic":"Soybean genetics and cultivation","field":"Agricultural and Biological Sciences","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Selection (genetic algorithm); Genome; Genetic diversity; Genetics; Biology; Computational biology; Evolutionary biology; Genetic marker; Computer science; Artificial intelligence; Gene; Medicine; Population","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":[0.0006279567,0.000285744,0.0004177195,0.001637739,0.0003398236,0.000478119,0.0006745787,0.0003370182,0.001336356],"category_scores_gemma":[0.0004588158,0.000345218,0.0003555035,0.002039169,0.0002295071,0.0004338773,0.0003753475,0.001038988,0.0002472213],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006697318,"about_ca_system_score_gemma":0.000361303,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003762124,"about_ca_topic_score_gemma":0.005054403,"domain_scores_codex":[0.9997954,0.00005663677,0.00001395094,0.00005839712,0.00004801903,0.00002759813],"domain_scores_gemma":[0.9997768,0.00009035089,0.00005328725,0.00001968542,0.00002135858,0.00003863875],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00216503,0.0002327847,0.008575357,0.000326067,0.0001432589,0.0004959136,0.0003856089,0.003301832,0.8806624,0.006351914,0.0005817933,0.09677793],"study_design_scores_gemma":[0.001341009,0.001831147,0.5178164,0.0002671675,0.001358171,0.003111804,0.00112117,0.0257733,0.3197806,0.03271746,0.09462118,0.0002605919],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9249038,0.005714126,0.05429083,0.0007869995,0.00006473281,0.0001398694,0.00375963,0.0003138051,0.01002626],"genre_scores_gemma":[0.9438192,0.003719632,0.0408426,0.0001738629,0.0000480523,0.0000994343,0.00672667,0.0001100257,0.004460535],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003762124,"threshold_uncertainty_score":0.007480443,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02054186982485224,"score_gpt":0.2068373840050693,"score_spread":0.1862955141802171,"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."}}