{"id":"W4245132882","doi":"10.33423/jabe.v21i6.2403","title":"Discount Coupons in Rural Markets","year":2019,"lang":"en","type":"article","venue":"Journal of Applied Business and Economics","topic":"ICT Impact and Policies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Coupon; Business; Distribution (mathematics); Rural area; Advertising; Marketing; Agricultural economics; Economics; Finance; Mathematics; Medicine","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":[0.001562737,0.0001656098,0.0002358845,0.001393891,0.001112332,0.001408185,0.0003468863,0.0004897967,0.01057684],"category_scores_gemma":[0.005071414,0.0002821255,0.0001436106,0.001665325,0.00082939,0.001772825,0.0009569277,0.0004731651,0.0006924401],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008510408,"about_ca_system_score_gemma":0.0006007977,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008581712,"about_ca_topic_score_gemma":0.01240326,"domain_scores_codex":[0.9989781,0.0004602096,0.00002781946,0.0001205512,0.0001724458,0.0002407949],"domain_scores_gemma":[0.9959487,0.001506399,0.001284322,0.000170031,0.0006546191,0.0004358271],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001128973,0.0008282769,0.8073199,0.0004969732,0.00006702446,0.002355509,0.03095331,0.002106511,0.006843382,0.01425108,0.006588533,0.1270606],"study_design_scores_gemma":[0.00004173137,0.0005999117,0.9203079,0.0001532759,0.00003003955,0.00132266,0.03910288,0.003267973,0.001433102,0.003407107,0.0302807,0.00005268256],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9925522,0.0002701674,0.0005182524,0.0002266291,0.000004464812,0.00003564171,0.0001418474,0.000006045282,0.006244724],"genre_scores_gemma":[0.9981399,0.0001706968,0.0002418378,0.00004582035,0.000005088611,0.00001031409,0.00005760032,0.000003523387,0.001325108],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01057684,"threshold_uncertainty_score":0.03538305,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004090755863162617,"score_gpt":0.1770617182478951,"score_spread":0.1729709623847324,"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."}}