{"id":"W2583755215","doi":"10.1016/j.jeca.2017.06.002","title":"Vertical price relationships between different cuts and quality grades in the U.S. beef marketing channel: A wholesale-retail analysis","year":2017,"lang":"en","type":"article","venue":"The Journal of Economic Asymmetries","topic":"Economics of Agriculture and Food Markets","field":"Economics, Econometrics and Finance","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Agricultural Marketing Service","keywords":"Retail market; Quality (philosophy); Wholesale market; Product (mathematics); Wholesale price index; Economics; Product differentiation; Channel (broadcasting); Business; Price level; Marketing; Agricultural economics; Microeconomics; Monetary economics; Mid price; Mathematics","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":[0.0007615214,0.0002028819,0.0003320657,0.0008952974,0.0004474255,0.001588671,0.0004451654,0.000735625,0.01288438],"category_scores_gemma":[0.004478335,0.0003773991,0.0006406634,0.001622732,0.0007748658,0.00131963,0.0007882373,0.001133224,0.0008127096],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000914849,"about_ca_system_score_gemma":0.0005584198,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04463607,"about_ca_topic_score_gemma":0.06181372,"domain_scores_codex":[0.9996241,0.00009193444,0.00002118769,0.00006624449,0.00006182596,0.0001345745],"domain_scores_gemma":[0.9941806,0.003481671,0.001023464,0.0002342849,0.0006605736,0.0004193895],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001664757,0.0005767127,0.965279,0.00004343287,0.0002820709,0.000279309,0.0006137242,0.004664931,0.002115867,0.006911127,0.002914902,0.01465411],"study_design_scores_gemma":[0.00007709854,0.000180574,0.9824869,0.00001998885,0.0002149469,0.0001033828,0.001692796,0.01087695,0.0004290896,0.003035458,0.0008551115,0.00002773809],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9976901,0.00008975052,0.0002482508,0.0001839686,0.000007283939,0.000005276636,0.0004370391,0.000007358612,0.001331025],"genre_scores_gemma":[0.9983449,0.00004838631,0.00009603203,0.00003639227,0.000008317972,0.000002648246,0.0004579553,0.000005889939,0.0009995212],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04463607,"threshold_uncertainty_score":0.08875251,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09450965753988692,"score_gpt":0.272935897342616,"score_spread":0.178426239802729,"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."}}