{"id":"W4247922211","doi":"10.4018/978-1-60960-818-7.ch609","title":"Forecasting Supply Chain Demand Using Machine Learning Algorithms","year":2011,"lang":"en","type":"book-chapter","venue":"Machine Learning","topic":"Forecasting Techniques and Applications","field":"Decision Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"HEC Montréal","funders":"","keywords":"Supply chain; Ranking (information retrieval); Computer science; Demand forecasting; Quality (philosophy); Support vector machine; Supply and demand; Machine learning; Competition (biology); Noise (video); Artificial intelligence; Supply chain management; Distortion (music); Demand management; Operations research; Algorithm; Engineering; Marketing; Business; Economics; Microeconomics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts","research_integrity","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.004765304,0.0009055497,0.001174062,0.0009721728,0.001495654,0.000491254,0.001444872,0.0006327452,0.004058463],"category_scores_gemma":[0.00216123,0.0007820661,0.00055947,0.0003740038,0.0002605,0.0002621013,0.001075605,0.002939354,0.0003495566],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001572921,"about_ca_system_score_gemma":0.0001088092,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009040375,"about_ca_topic_score_gemma":0.0001973323,"domain_scores_codex":[0.9941201,0.0002707691,0.001648514,0.001565174,0.001570061,0.000825343],"domain_scores_gemma":[0.9952042,0.001172203,0.001898822,0.0009693826,0.0004405518,0.0003148981],"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":[0.0001508865,0.0001215511,0.02476709,0.0001436857,0.00034037,0.0003751051,0.002635225,0.02260477,0.0005728902,0.06624868,0.002392773,0.879647],"study_design_scores_gemma":[0.0002389789,0.0001823472,0.00003160045,0.0002814654,0.00008362689,0.0002180085,0.00002483483,0.5766402,0.00007764788,0.044714,0.3767635,0.0007437942],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.003882773,0.00490461,0.2280865,0.0005544944,0.0007359734,0.001711538,0.0002423558,0.001986711,0.757895],"genre_scores_gemma":[0.1223605,0.0002780431,0.07620604,0.0001950764,0.001028265,0.00005217086,0.0004085735,0.0005331744,0.7989382],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.8789032,"threshold_uncertainty_score":0.9998043,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1585308921770113,"score_gpt":0.3428885349061645,"score_spread":0.1843576427291532,"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."}}