{"id":"W4320516457","doi":"10.5539/jas.v15n3p58","title":"Molecular Markers in Plant Breeding","year":2023,"lang":"en","type":"article","venue":"Journal of Agricultural Science","topic":"Genetic Mapping and Diversity in Plants and Animals","field":"Biochemistry, Genetics and Molecular Biology","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Molecular marker; Molecular breeding; Biology; Marker-assisted selection; Quantitative trait locus; Backcrossing; Genetic marker; Selection (genetic algorithm); Computational biology; Gene mapping; Genomics; Genome; Plant breeding; Trait; Genetics; Biotechnology; Gene; 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":[0.004162784,0.001007347,0.002171121,0.002573191,0.0008248189,0.00307263,0.001811389,0.002483516,0.004478477],"category_scores_gemma":[0.003235482,0.0005998131,0.0008335174,0.004354403,0.001958708,0.002734386,0.001656717,0.004713454,0.005662064],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001184116,"about_ca_system_score_gemma":0.001021361,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008603398,"about_ca_topic_score_gemma":0.0005868896,"domain_scores_codex":[0.9966672,0.001299688,0.0002373285,0.0007745409,0.0008870336,0.0001341768],"domain_scores_gemma":[0.9984193,0.0007533922,0.0002346995,0.0002340529,0.0002725054,0.00008600165],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001846678,0.0001534909,0.002605007,0.004339764,0.0002144482,0.0007113054,0.0004022234,0.002318429,0.08024153,0.08917283,0.0253417,0.7943147],"study_design_scores_gemma":[0.00003486771,0.0002180145,0.002676397,0.001108299,0.0001616155,0.002260049,0.0001690318,0.00158311,0.01925431,0.04812337,0.924305,0.000106024],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"methods","genre_scores_codex":[0.007902307,0.5845208,0.358873,0.006977054,0.005003534,0.0003574741,0.001625806,0.00129112,0.03344895],"genre_scores_gemma":[0.07612099,0.4318523,0.4466441,0.00510371,0.003429696,0.0008486834,0.003416878,0.000554478,0.03202923],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.004478477,"threshold_uncertainty_score":0.02201521,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01201490533114073,"score_gpt":0.2223485430346457,"score_spread":0.210333637703505,"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."}}