{"id":"W1550691097","doi":"10.1109/iccsit.2010.5563664","title":"Notice of Retraction: Improving inventory management through automated dynamic promotion scheduling","year":2010,"lang":"en","type":"article","venue":"","topic":"Scheduling and Timetabling Solutions","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Notice; Computer science; Scheduling (production processes); Inventory management; Promotion (chess); Operations research; Operations management; Engineering; Political science","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":{"nature":"Retraction","reason":"Breach of Policy by Author;Investigation by Journal/Publisher;Notice - Limited or No Information;","date":"9/7/2010 0:00","openalex_flagged":false},"screen":null,"direct_labels":[{"model":"gemma","categories":[],"domain":null,"study_design":"not_applicable","genre":"editorial","about_ca_system":false,"about_ca_topic":false,"confidence":"high","status":"direct model label, unvalidated"},{"model":"gpt","categories":[],"domain":null,"study_design":"not_applicable","genre":"editorial","about_ca_system":false,"about_ca_topic":false,"confidence":"high","status":"direct model label, unvalidated"}],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003140911,0.0001365387,0.0002213336,0.0003039302,0.0002575852,0.0001564613,0.0004930802,0.0001705591,0.0005871025],"category_scores_gemma":[0.001746621,0.0001096084,0.0001288216,0.001151633,0.0001189683,0.0006631647,0.0001199294,0.0004006201,0.000350828],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003658127,"about_ca_system_score_gemma":0.00006727414,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009364615,"about_ca_topic_score_gemma":0.00009909623,"domain_scores_codex":[0.9973757,0.0000969649,0.0007194211,0.0004737595,0.001051412,0.0002827026],"domain_scores_gemma":[0.9980882,0.0002690576,0.0003821229,0.0007368758,0.0004425907,0.0000811834],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001716578,0.003407383,0.01242974,0.0003509391,0.0005750571,0.00005332663,0.002914169,0.02867406,0.3963945,0.3623988,0.00457082,0.1880596],"study_design_scores_gemma":[0.0005264183,0.00008125076,0.01538015,0.00003868538,0.00008394912,0.00002158468,0.001071678,0.9568973,0.006312469,0.01757408,0.001753358,0.000259114],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8753591,0.00003943834,0.09382484,0.001191173,0.00329743,0.0002900641,0.000003561698,0.0007048125,0.02528959],"genre_scores_gemma":[0.8507557,0.000003758225,0.1468938,0.00004298685,0.00006453986,0.000008855647,0.000003428979,0.00001151063,0.002215406],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9282232,"threshold_uncertainty_score":0.642836,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07673058513063866,"score_gpt":0.3924113847870677,"score_spread":0.315680799656429,"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."}}