{"id":"W4393900941","doi":"10.3390/jrfm17040143","title":"Forecasting Agriculture Commodity Futures Prices with Convolutional Neural Networks with Application to Wheat Futures","year":2024,"lang":"en","type":"article","venue":"Journal of risk and financial management","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Futures contract; Commodity; Economics; Financial economics; Futures market; Agriculture; Convolutional neural network; Computer science; Artificial intelligence; Geography; Finance","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002631186,0.0001950051,0.0003215249,0.000319058,0.0004142613,0.0004113074,0.0003655675,0.00006746194,0.00001110772],"category_scores_gemma":[0.0003214242,0.0001002884,0.0000866509,0.001020136,0.0000733414,0.0002985769,0.0001354945,0.0003956912,0.000001498156],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000530201,"about_ca_system_score_gemma":0.00003196367,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002278985,"about_ca_topic_score_gemma":0.0002679557,"domain_scores_codex":[0.9977417,0.0001531058,0.0005211705,0.0003456226,0.0009951579,0.0002432193],"domain_scores_gemma":[0.9982064,0.0007393006,0.0003968977,0.0001819744,0.000327541,0.0001478465],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0006178546,0.00003462353,0.01046289,0.00002913467,0.00003999349,0.000091399,0.0006685582,0.04211436,0.000003899923,0.002499528,0.01327935,0.9301584],"study_design_scores_gemma":[0.001001785,0.001220581,0.6801004,0.0004190683,0.0003313998,0.0006570096,0.002167336,0.09943613,0.0000139611,0.009291617,0.2048812,0.0004795424],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2374761,0.001500955,0.7591681,0.000399797,0.0005880678,0.0003070035,0.00001153641,0.00002118566,0.0005272632],"genre_scores_gemma":[0.9494218,0.00009959703,0.04891883,0.0001534666,0.001268867,0.00001738868,0.000002062827,0.00001194153,0.0001060135],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9296789,"threshold_uncertainty_score":0.4089641,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02480207225900299,"score_gpt":0.2965223732028129,"score_spread":0.2717203009438099,"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."}}