{"id":"W4401352192","doi":"10.32854/agrop.v17i7.2811","title":"Determining co-movements of tomato prices in the United States and macroeconomic variables in Mexico for 2023","year":2024,"lang":"en","type":"article","venue":"Agro Productividad","topic":"Global Trade and Competitiveness","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Economics; Environmental science; Agricultural economics; Monetary economics","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":[0.0004538127,0.0001643706,0.0001426607,0.001078777,0.0002618643,0.0007433018,0.0001824292,0.000181845,0.00138738],"category_scores_gemma":[0.001331534,0.00009750439,0.0002633453,0.001589144,0.0001172601,0.0003247819,0.0003197659,0.0002641459,0.0001354001],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001070369,"about_ca_system_score_gemma":0.0005252635,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1135982,"about_ca_topic_score_gemma":0.1208119,"domain_scores_codex":[0.9998689,0.00002049324,0.00001076504,0.0000387823,0.00003450843,0.00002653907],"domain_scores_gemma":[0.9992174,0.0001230106,0.0003914475,0.00002693312,0.000198388,0.00004283732],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002622147,0.00001352247,0.9962818,0.000007660178,0.00005661417,0.00004087301,0.00006511995,0.0003210178,0.0001229505,0.00009128542,0.0004043749,0.002568569],"study_design_scores_gemma":[0.000001087911,0.000009567796,0.9983317,0.000003743549,0.0000196489,0.00001123907,0.0002546071,0.0008321798,0.00006662535,0.00001682716,0.0004508163,0.000002053546],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9962181,0.0001305329,0.0001944966,0.0001120672,0.000006579486,0.000008163896,0.002162718,0.000008924314,0.001158311],"genre_scores_gemma":[0.996931,0.0001380574,0.000288974,0.00001111103,0.000009018487,0.000009632697,0.00211777,0.000003184042,0.0004912562],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1135982,"threshold_uncertainty_score":0.2258741,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01956885370751981,"score_gpt":0.2540242039884987,"score_spread":0.2344553502809789,"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."}}