{"id":"W4392200264","doi":"10.18280/isi.290129","title":"Demand Prediction for Food and Beverage SMEs Using SARIMAX and Weather Data","year":2024,"lang":"en","type":"article","venue":"Ingénierie des systèmes d information","topic":"Food Supply Chain Traceability","field":"Agricultural and Biological Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Direktorat Jenderal Pendidikan Tinggi; Kementerian Pendidikan, Kebudayaan, Riset, dan Teknologi","keywords":"Weather prediction; Demand forecasting; Business; Computer science; Meteorology; Marketing; Geography","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":[0.0004458467,0.0001028464,0.0001049432,0.00002190582,0.0002308904,0.000370225,0.00009638852,0.0000843439,0.00001357668],"category_scores_gemma":[0.0001093356,0.00004711511,0.00002082661,0.0001300799,0.00008825805,0.002319962,0.00009289116,0.00005739039,0.00000171661],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000622092,"about_ca_system_score_gemma":0.00001136156,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009118405,"about_ca_topic_score_gemma":0.0001391431,"domain_scores_codex":[0.9993126,0.00002737207,0.0002411282,0.0001717015,0.00009873187,0.0001484684],"domain_scores_gemma":[0.9996338,0.0001532194,0.00004803936,0.00007095365,0.00004670194,0.00004722124],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001311568,0.00004564827,0.02362007,0.001793858,0.0001610808,0.00000120846,0.006487661,0.00007629128,0.02079727,0.003877484,0.001639712,0.9413686],"study_design_scores_gemma":[0.001047345,0.00243715,0.2753272,0.0008925042,0.000225578,0.000305947,0.009533987,0.5404765,0.004032224,0.03990631,0.1246646,0.001150738],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.994357,0.0009629942,0.002454774,0.0001978063,0.0001506,0.0004229488,0.001102587,0.0001217276,0.0002295409],"genre_scores_gemma":[0.998444,0.00004458287,0.0008963289,0.00005045451,0.000103648,0.00001763725,0.0004313259,0.00000108991,0.00001099164],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9402178,"threshold_uncertainty_score":0.3570088,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03681921674110779,"score_gpt":0.2426103867945947,"score_spread":0.2057911700534869,"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."}}