{"id":"W4393054540","doi":"10.1002/csc2.21231","title":"Two types of biplots to integrate multi‐trial and multi‐trait information for genotype selection","year":2024,"lang":"en","type":"article","venue":"Crop Science","topic":"Genetics and Plant Breeding","field":"Agricultural and Biological Sciences","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"","keywords":"Biplot; Biology; Selection (genetic algorithm); Trait; Genotype; Evolutionary biology; Genetics; Computational biology; Biotechnology; Artificial intelligence; Gene; Computer science","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004699443,0.001500742,0.001118698,0.003828693,0.0005293972,0.001693717,0.000700858,0.000424902,0.00530369],"category_scores_gemma":[0.008858388,0.0003421052,0.001305212,0.004589435,0.0004435701,0.0008382275,0.001027636,0.001422715,0.001214462],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005454581,"about_ca_system_score_gemma":0.001050015,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003741425,"about_ca_topic_score_gemma":0.004013506,"domain_scores_codex":[0.9970836,0.001459191,0.0002272677,0.0004285935,0.0005863667,0.0002150176],"domain_scores_gemma":[0.9936466,0.002701307,0.0006150226,0.001265808,0.001471795,0.0002994889],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00491922,0.001707767,0.08479297,0.001184108,0.001431924,0.0004684322,0.0009177443,0.04074304,0.05347189,0.009132357,0.03666632,0.7645642],"study_design_scores_gemma":[0.001019759,0.002608383,0.4205877,0.0003011862,0.0005085322,0.000465091,0.0009966864,0.4309788,0.03744053,0.01628769,0.08819821,0.0006074446],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2866991,0.0005755058,0.6582471,0.0004245698,0.0004646358,0.001378965,0.0277958,0.01829237,0.006122],"genre_scores_gemma":[0.4176632,0.0001835272,0.5564743,0.0001607689,0.00007850987,0.001981532,0.01905755,0.001828911,0.002571666],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00530369,"threshold_uncertainty_score":0.02485329,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04678210395958533,"score_gpt":0.2764077286261661,"score_spread":0.2296256246665808,"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."}}