{"id":"W1584079741","doi":"10.1139/g10-080","title":"Analysis of yield and oil from a series of canola breeding trials. Part II. Exploring variety by environment interaction using factor analysisThis article is one of a selection of papers from the conference “Exploiting Genome-wide Association in Oilseed Brassicas: a model for genetic improvement of major OECD crops for sustainable farming”.","year":2010,"lang":"en","type":"article","venue":"Genome","topic":"Genetics and Plant Breeding","field":"Agricultural and Biological Sciences","cited_by":120,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Grains Research and Development Corporation","keywords":"Biplot; Gene–environment interaction; Variety (cybernetics); Selection (genetic algorithm); Context (archaeology); Biology; Machine learning; Computer science; Data science; Data mining; Biotechnology; Artificial intelligence; Genetics; Genotype","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":[0.003208576,0.0006058603,0.0006239422,0.001873366,0.0003929446,0.0004820725,0.0003817167,0.0003763499,0.001624188],"category_scores_gemma":[0.006278735,0.0001492099,0.0008762596,0.001966461,0.0003408663,0.0003123748,0.0004118444,0.0006402075,0.0003822872],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004965426,"about_ca_system_score_gemma":0.0002600004,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002725965,"about_ca_topic_score_gemma":0.004073152,"domain_scores_codex":[0.998524,0.000704672,0.0000898737,0.0002587549,0.000354353,0.00006839487],"domain_scores_gemma":[0.9919768,0.005171299,0.001020806,0.0009349099,0.0006499158,0.0002462926],"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":[0.005398308,0.001595725,0.2290026,0.0008852422,0.002682382,0.0009261224,0.001056271,0.0250305,0.4796326,0.002068172,0.003643557,0.2480786],"study_design_scores_gemma":[0.0001043178,0.003371105,0.8921946,0.00002304682,0.000467593,0.0005749104,0.0003036844,0.01697659,0.07721111,0.001674034,0.006952245,0.000146808],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9555702,0.0009277412,0.03362433,0.00008457746,0.00004411133,0.0002066314,0.007317421,0.0004187574,0.001806161],"genre_scores_gemma":[0.9551808,0.0003715202,0.029347,0.00009892755,0.00002151703,0.00038536,0.0116414,0.0001615922,0.00279193],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003208576,"threshold_uncertainty_score":0.01696879,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06936683526738446,"score_gpt":0.2229609687129752,"score_spread":0.1535941334455908,"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."}}