{"id":"W2065296437","doi":"10.4141/cjps10004","title":"Bayesian data analysis for agricultural experiments","year":2010,"lang":"en","type":"article","venue":"Canadian Journal of Plant Science","topic":"Genetics and Plant Breeding","field":"Agricultural and Biological Sciences","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of Food and Agriculture; U.S. Department of Agriculture","keywords":"Markov chain Monte Carlo; Bayesian probability; Computer science; Variable-order Bayesian network; Bayesian average; Algorithm; Bayesian statistics; Data mining; Bayesian inference; Machine learning; 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":[0.07576878,0.002088762,0.004635492,0.006642057,0.002050572,0.004707979,0.003938616,0.002685799,0.01325173],"category_scores_gemma":[0.2074919,0.00162049,0.004226148,0.008291258,0.004023504,0.004782988,0.003934179,0.00681909,0.003009571],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004399818,"about_ca_system_score_gemma":0.006502871,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004256256,"about_ca_topic_score_gemma":0.003377282,"domain_scores_codex":[0.9268546,0.05341874,0.004090314,0.007062578,0.007951355,0.0006223497],"domain_scores_gemma":[0.8402019,0.1238737,0.009819395,0.01460677,0.0105832,0.0009150919],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.001066448,0.0004087793,0.01630164,0.006734151,0.003334014,0.0008434645,0.002789272,0.08555607,0.00879838,0.4111922,0.03211131,0.4308642],"study_design_scores_gemma":[0.0004484462,0.0005130832,0.01366637,0.001406199,0.0007725799,0.0006766026,0.0006299831,0.3013919,0.004916541,0.5807578,0.09443598,0.0003845915],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001575504,0.0004311187,0.9945812,0.0002335823,0.0000747798,0.00056453,0.0006809671,0.0006870847,0.001171317],"genre_scores_gemma":[0.02527027,0.0005400408,0.9672248,0.0002797674,0.00008377754,0.004236657,0.001220243,0.0004364821,0.0007079664],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.07576878,"threshold_uncertainty_score":0.4007084,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06280947615664628,"score_gpt":0.2419887753068112,"score_spread":0.1791792991501649,"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."}}