{"id":"W7101221478","doi":"","title":"Optimal Tracking and Testing of U.S. and Canadian Herds for BSE: A Value-of-Information (VOI) Approach","year":2016,"lang":"en","type":"article","venue":"","topic":"Economic and Environmental Valuation","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Tracking (education); Tracking system; Matching (statistics); Herd","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.009877454,0.0005982789,0.00168823,0.002935812,0.001489879,0.002784571,0.003050005,0.002533572,0.006765562],"category_scores_gemma":[0.03509285,0.0008768377,0.00114743,0.00238672,0.002116035,0.002426373,0.001325933,0.001666582,0.000533673],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.009068508,"about_ca_system_score_gemma":0.01098865,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.5965403,"about_ca_topic_score_gemma":0.6298109,"domain_scores_codex":[0.9973219,0.001057907,0.0001074945,0.0005416811,0.0003509645,0.0006200433],"domain_scores_gemma":[0.9894951,0.006742189,0.001093644,0.0007970444,0.001138892,0.0007331452],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001032923,0.0003492107,0.2060113,0.0003506159,0.0006959199,0.0003930504,0.0006577768,0.3600828,0.001250965,0.1034101,0.02525808,0.3005074],"study_design_scores_gemma":[0.0001153517,0.000314636,0.06645781,0.0001977753,0.0003498365,0.0001574606,0.001075396,0.8445956,0.001263946,0.07974324,0.005623024,0.0001058431],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.540245,0.00366014,0.3542679,0.01683379,0.0003104578,0.00124314,0.01382932,0.001293179,0.06831696],"genre_scores_gemma":[0.9596233,0.0004441566,0.03127267,0.0003173896,0.00004181925,0.0000593379,0.001489025,0.00004274589,0.006709511],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4034597,"threshold_uncertainty_score":0.8116714,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09481194377696373,"score_gpt":0.2079826310885717,"score_spread":0.1131706873116079,"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."}}