{"id":"W1484796914","doi":"10.1002/for.1242","title":"The Accuracy of Non‐traditional versus Traditional Methods of Forecasting Lumpy Demand","year":2011,"lang":"en","type":"article","venue":"Journal of Forecasting","topic":"Forecasting Techniques and Applications","field":"Decision Sciences","cited_by":53,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Demand forecasting; Computer science; Product (mathematics); Econometrics; Operations research; Economics; Mathematics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007503316,0.0006108042,0.0006822831,0.001564292,0.0002463573,0.00119154,0.001016965,0.0007710294,0.0009127632],"category_scores_gemma":[0.02544841,0.0002355694,0.0006310458,0.001069786,0.0003878268,0.00198674,0.0006763905,0.0006812536,0.0003121846],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005771049,"about_ca_system_score_gemma":0.0004888559,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005899603,"about_ca_topic_score_gemma":0.00515587,"domain_scores_codex":[0.9975485,0.001107996,0.0002343909,0.0003678684,0.0006473469,0.00009382304],"domain_scores_gemma":[0.9707245,0.02313155,0.001672773,0.001693057,0.002492107,0.0002860801],"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":[0.001739343,0.0003214797,0.1289005,0.0005047009,0.0009640118,0.0001100686,0.0004237263,0.3741851,0.003951577,0.002771787,0.002389734,0.4837379],"study_design_scores_gemma":[0.00005422117,0.0003023412,0.0424749,0.00005745102,0.0001026158,0.00007917511,0.0001274188,0.9512684,0.002824962,0.001952197,0.0007082202,0.00004815775],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8746459,0.003136353,0.1163876,0.0004594487,0.0001965346,0.00006577211,0.0004842293,0.0003847553,0.004239358],"genre_scores_gemma":[0.9704744,0.000576102,0.02768956,0.00003982043,0.00007534758,0.00002267048,0.0003818052,0.00002163348,0.0007187148],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007503316,"threshold_uncertainty_score":0.03968179,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.6489373783846855,"score_gpt":0.4564700294472598,"score_spread":0.1924673489374256,"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."}}