{"id":"W4388925186","doi":"10.23977/acss.2023.070916","title":"A study on vegetable commodity replenishment considering single item quantity limitations—based on gray prediction and linear regression modeling","year":2023,"lang":"en","type":"article","venue":"Advances in Computer Signals and Systems","topic":"Supply Chain and Inventory Management","field":"Business, Management and Accounting","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Revenue; Profit (economics); Computer science; Operations research; Linear regression; Linear programming; Econometrics; Commodity; Economics; Microeconomics; Mathematics; Machine learning; Algorithm","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":[0.0008548068,0.0001913363,0.0002759872,0.0003638525,0.0002558175,0.0002900603,0.00009147357,0.00003992053,0.000001542162],"category_scores_gemma":[0.00004752984,0.0001628412,0.00003080092,0.0003289858,0.00002480455,0.0006675073,0.0001253742,0.0001133375,0.00001062804],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003686182,"about_ca_system_score_gemma":0.000004991756,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001010615,"about_ca_topic_score_gemma":0.00005793456,"domain_scores_codex":[0.9985486,0.00007263586,0.0003991511,0.0004458771,0.0003163333,0.0002174332],"domain_scores_gemma":[0.9992903,0.000240192,0.0001543392,0.0002346636,0.00006103296,0.00001943047],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006911193,0.0003885269,0.07829411,0.0004423734,0.00001952312,0.00003595623,0.0002687856,0.9145058,0.00004940572,0.001052197,0.0004520939,0.004422116],"study_design_scores_gemma":[0.0008567075,0.0002127434,0.002298541,0.0007411296,0.00001387736,6.153782e-7,0.0007947151,0.9911099,0.0000109358,0.0002921395,0.003504564,0.0001641276],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9694211,0.000587924,0.02636967,0.0001899715,0.001494037,0.001043656,0.000004791335,0.0002440865,0.0006446954],"genre_scores_gemma":[0.9988841,0.00007248967,0.0001739441,0.000313475,0.0004166522,0.00008130215,0.00002500163,0.00001737587,0.00001565561],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0766041,"threshold_uncertainty_score":0.6640472,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1185561747517916,"score_gpt":0.2880481098661447,"score_spread":0.1694919351143531,"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."}}