{"id":"W2772390935","doi":"10.1038/s41598-017-17631-4","title":"Optimization of cotton variety registration criteria aided with a genotype-by-trait biplot analysis","year":2017,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Research in Cotton Cultivation","field":"Agricultural and Biological Sciences","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"","keywords":"Biplot; Trait; Selection (genetic algorithm); Yield (engineering); Biotechnology; Index (typography); Quality (philosophy); Cultivar; Statistics; Biology; Mathematics; Computer science; Genotype; Agronomy; Artificial intelligence; Genetics","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":[0.004989511,0.001247784,0.001427323,0.002355403,0.0004101706,0.001143244,0.0005598603,0.0002694008,0.001296258],"category_scores_gemma":[0.006123037,0.000228973,0.001160613,0.002899133,0.0003272578,0.0004769928,0.0006092629,0.0007066571,0.0003356682],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004646722,"about_ca_system_score_gemma":0.001035142,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002372973,"about_ca_topic_score_gemma":0.002931645,"domain_scores_codex":[0.9971826,0.00130652,0.0002997326,0.0005935104,0.0003969916,0.000220632],"domain_scores_gemma":[0.9966136,0.001568533,0.0004013396,0.0004099483,0.000837798,0.000168762],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.005117303,0.002054939,0.1505238,0.0009668778,0.001362708,0.0005323808,0.001773129,0.04275241,0.3875515,0.002382121,0.002262572,0.4027202],"study_design_scores_gemma":[0.0002692443,0.004577758,0.5998695,0.00005827906,0.000690777,0.0003566432,0.0006644417,0.3225125,0.06101993,0.001596742,0.008122334,0.0002618723],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.797609,0.0002066318,0.1982496,0.00005643792,0.00005133079,0.0003756766,0.0013312,0.001214566,0.0009055861],"genre_scores_gemma":[0.7403399,0.00008515686,0.2543381,0.00002671562,0.00001365982,0.0006128938,0.003409451,0.0004445986,0.0007293873],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004989511,"threshold_uncertainty_score":0.02638733,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03585733059710036,"score_gpt":0.2896208100860527,"score_spread":0.2537634794889523,"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."}}