{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004601527,0.0001066682,0.00009107884,0.00008517259,0.00007443203,0.00004493355,0.0001051549,0.00004938973,0.000002639146],"category_scores_gemma":[0.000104441,0.0001022433,0.00002476738,0.000101343,0.000008112578,0.0001611651,0.00001411185,0.0006873313,0.00004129021],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006560255,"about_ca_system_score_gemma":0.0002314574,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000313525,"about_ca_topic_score_gemma":0.00003476003,"domain_scores_codex":[0.9986448,0.0000149593,0.0001500035,0.00009738572,0.0001493138,0.0009435433],"domain_scores_gemma":[0.9996728,0.000008995519,0.00003051431,0.00008545593,0.00007445433,0.0001278389],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00001056464,0.00001247914,0.00003926233,0.000005863429,0.00002059923,0.000001315373,0.0001184157,0.9772804,0.0002857393,0.004764237,0.00006544435,0.01739563],"study_design_scores_gemma":[0.002266545,0.001392415,0.0003138491,0.0001657995,0.00007864799,0.0007058505,0.001909478,0.3128519,0.02106971,0.6510419,0.00704354,0.001160365],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09594885,0.0006767591,0.9017389,0.0003030706,0.0002805442,0.000119418,8.8669e-7,0.0001989808,0.0007326099],"genre_scores_gemma":[0.9899686,0.0003532055,0.008981693,0.00001757533,0.0002756823,0.000006369065,0.000004407327,0.00003123718,0.0003612292],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8940197,"threshold_uncertainty_score":0.4169359,"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."}}