{"id":"W2219302586","doi":"10.2139/ssrn.2638396","title":"Scheduling Promotion Vehicles to Boost Pro fits","year":2015,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Advanced Manufacturing and Logistics Optimization","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"","keywords":"Scheduling (production processes); Promotion (chess); Computer science; Business; Operations research; Economics; Engineering; Operations management; Political science; Law","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":[0.0007798579,0.001071834,0.001270415,0.001031108,0.0008636836,0.001299916,0.001396927,0.001003326,0.01244829],"category_scores_gemma":[0.002255765,0.0006254722,0.0005591665,0.0008411835,0.0005206123,0.001046041,0.0009100735,0.001355038,0.001957688],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008357466,"about_ca_system_score_gemma":0.002226028,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004092656,"about_ca_topic_score_gemma":0.007154387,"domain_scores_codex":[0.999563,0.0000938228,0.000007112416,0.00005899035,0.0000496562,0.0002272526],"domain_scores_gemma":[0.9988461,0.0003410107,0.0001014144,0.00009489658,0.0002174572,0.0003991291],"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":[0.006432191,0.001978921,0.008794693,0.0004290913,0.0001035389,0.0002858357,0.0002930225,0.7033887,0.01700448,0.01919445,0.02244553,0.2196495],"study_design_scores_gemma":[0.0001103601,0.0009306975,0.00173511,0.00002008342,0.0000409348,0.00004672988,0.0003779348,0.9798729,0.003536961,0.008692292,0.0046171,0.00001883246],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6897195,0.0008900731,0.2601919,0.001339297,0.0008432855,0.0006732757,0.0005618521,0.002874189,0.04290663],"genre_scores_gemma":[0.9749997,0.000107153,0.0160677,0.00007538123,0.00005691506,0.00006248459,0.0001608225,0.00009174064,0.008378164],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01244829,"threshold_uncertainty_score":0.04164362,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02111306521248326,"score_gpt":0.2440476249799265,"score_spread":0.2229345597674433,"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."}}