{"id":"W2105363443","doi":"10.1017/s1074070800026894","title":"Predicting Pork Supplies: An Application of Multiple Forecast Encompassing","year":2004,"lang":"en","type":"article","venue":"Journal of Agricultural and Applied Economics","topic":"Forecasting Techniques and Applications","field":"Decision Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Univariate; Quarter (Canadian coin); Competitor analysis; Econometrics; Production (economics); Forecast error; Horizon; Statistics; Service (business); Economics; Operations research; Operations management; Computer science; Mathematics; Marketing; Business; Microeconomics; Geography; Multivariate statistics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007279959,0.0001220327,0.0003265067,0.0000951776,0.0001583667,0.0001217637,0.0003783233,0.00007216894,0.000005507992],"category_scores_gemma":[0.00006030668,0.00007329878,0.00009343617,0.000196033,0.00008880659,0.0003506331,0.00006726363,0.000145014,0.000003064767],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005422764,"about_ca_system_score_gemma":0.00003144291,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002707108,"about_ca_topic_score_gemma":0.00005407097,"domain_scores_codex":[0.9985159,0.00001041853,0.0009531621,0.0002045868,0.000175919,0.0001399848],"domain_scores_gemma":[0.9981538,0.0001807854,0.001192041,0.0001626284,0.0001875581,0.0001231515],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0002605278,0.0007754158,0.06151429,0.00004957468,0.0001273013,5.6592e-7,0.004901581,0.3718388,0.1007341,0.2231602,0.001000803,0.2356368],"study_design_scores_gemma":[0.002058397,0.0005403131,0.7135097,0.00007831571,0.00007259064,0.0004434476,0.01054425,0.003567368,0.07521614,0.1913659,0.002069989,0.0005336209],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9846591,0.00002260371,0.01404028,0.0004119992,0.00004736401,0.0001622797,0.00001492707,0.00001385687,0.0006276395],"genre_scores_gemma":[0.9736153,0.00002309571,0.02609587,0.00002931756,0.0002067241,0.000008219716,0.00000623128,0.000005849493,0.000009352504],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6519954,"threshold_uncertainty_score":0.2989038,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04839483524803331,"score_gpt":0.2883714163134513,"score_spread":0.2399765810654179,"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."}}