{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007582858,0.0000709618,0.0001291419,0.00008311498,0.0004042161,0.0002141013,0.00122827,0.00003768961,0.000167062],"category_scores_gemma":[0.0001311995,0.00002551383,0.00005528496,0.0006850492,0.0001186013,0.0002436975,0.00002935559,0.0001123305,0.000001954121],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001548462,"about_ca_system_score_gemma":0.0001702062,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002938718,"about_ca_topic_score_gemma":0.1434093,"domain_scores_codex":[0.9990956,0.000007907573,0.0001865121,0.0001862619,0.0002079765,0.0003157701],"domain_scores_gemma":[0.9989671,0.00007665231,0.000135063,0.00007728276,0.0001340079,0.0006099122],"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":[0.000004967635,0.000008527364,0.04996287,0.000001208059,0.00003746508,0.00001675875,0.0001153325,0.0000348854,0.9425662,0.0004542096,0.002412716,0.004384867],"study_design_scores_gemma":[0.0001567334,0.000220629,0.8975258,0.00001712739,0.0001472285,0.0003311638,0.0008160417,0.002471711,0.012706,0.0002182286,0.08506601,0.000323268],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9969679,0.00005101467,0.00009674664,0.0004625296,0.0006343365,0.00006311764,0.001440916,0.000002280624,0.0002811661],"genre_scores_gemma":[0.9983649,0.000003399551,0.001108207,0.00006351768,0.0002910627,5.353834e-7,0.0001374914,2.611671e-7,0.00003057567],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9298602,"threshold_uncertainty_score":0.8722213,"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."}}