{"id":"W4387987274","doi":"10.2139/ssrn.4582188","title":"Deep Neural Newsvendor","year":2023,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Newsvendor model; Artificial neural network; Computer science; Artificial intelligence; Business; Supply chain","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.02566284,0.0001866243,0.0003052611,0.0006781989,0.0004467064,0.0003180783,0.001352653,0.0000823221,0.0003243389],"category_scores_gemma":[0.01132785,0.000135629,0.0002848294,0.002398697,0.000073745,0.0003393595,0.0001929081,0.001957659,0.001352945],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004174948,"about_ca_system_score_gemma":0.0009728006,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001647314,"about_ca_topic_score_gemma":0.0003130178,"domain_scores_codex":[0.9930503,0.0008211149,0.0007121309,0.0004216357,0.001614229,0.00338056],"domain_scores_gemma":[0.9963756,0.002360919,0.0003436265,0.0004823563,0.0002473242,0.0001902258],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00006496385,0.0000146151,0.007163,6.947433e-7,0.00004265292,0.00002824915,0.0001633339,0.0009059675,0.0001929969,0.01845828,0.003116423,0.9698488],"study_design_scores_gemma":[0.0005277733,0.0002265446,0.01167391,0.000004217047,0.00001528853,0.001443034,0.002025969,0.03112166,0.00003693321,0.9423043,0.01041993,0.0002004513],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7663971,0.001622046,0.2048368,0.00639169,0.003862673,0.000278955,0.000002358777,0.0004076742,0.01620072],"genre_scores_gemma":[0.9784246,0.0002514457,0.001413375,0.0001913075,0.000804019,0.000006885491,0.000001302781,0.00003991718,0.01886716],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9696484,"threshold_uncertainty_score":0.9994246,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0827041286684934,"score_gpt":0.3990903082204118,"score_spread":0.3163861795519184,"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."}}