{"id":"W2981981251","doi":"10.1515/scid-2018-0005","title":"Bayesian Design of Agricultural Disease Transmission Experiments for Individual Level Models","year":2019,"lang":"en","type":"article","venue":"Statistical Communications in Infectious Diseases","topic":"Wheat and Barley Genetics and Pathology","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph; University of Calgary","funders":"","keywords":"Set (abstract data type); Computer science; Prior probability; Transmission (telecommunications); Bayesian probability; Design of experiments; Optimal design; Disease transmission; Mathematical optimization; Monte Carlo method; Function (biology); Statistics; Simulation; Machine learning; Artificial intelligence; Mathematics; Biology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001051563,0.0001131207,0.0001773761,0.00002086997,0.0001272041,0.00002520155,0.0003491182,0.00005330268,0.0001372939],"category_scores_gemma":[0.00004878105,0.00005178142,0.00006503557,0.000157191,0.0001086926,0.00006990602,0.00008022531,0.00007179379,0.000004225646],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001634803,"about_ca_system_score_gemma":0.00002337196,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000537114,"about_ca_topic_score_gemma":0.00003011934,"domain_scores_codex":[0.9990629,0.0001854986,0.0002569475,0.0001874091,0.0001230635,0.0001841723],"domain_scores_gemma":[0.9988024,0.0007787315,0.00005668099,0.0001624591,0.00007202962,0.0001276659],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0009525456,0.008029034,0.4164851,0.0003057547,0.0001889921,0.000004166317,0.001462997,0.01117062,0.1603386,0.1482116,0.001817987,0.2510327],"study_design_scores_gemma":[0.0009387471,0.0007725489,0.9133019,0.00008935013,0.00009659707,0.000002106991,0.000325965,0.009518788,0.0006985982,0.07365125,0.0002421091,0.0003620677],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8909973,0.002622715,0.09820541,0.0004973098,0.0001358319,0.001956818,0.00453219,0.00007644889,0.0009759559],"genre_scores_gemma":[0.993071,0.0002047918,0.005791346,0.00003755312,0.00001822293,0.0001576219,0.0006882848,0.000001534558,0.00002960041],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4968167,"threshold_uncertainty_score":0.2111585,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09442502405369296,"score_gpt":0.3090253331583999,"score_spread":0.214600309104707,"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."}}