{"id":"W4319337692","doi":"10.21203/rs.3.rs-2511227/v1","title":"Exam Room Timetabling Using MIP and SMAC","year":2023,"lang":"en","type":"preprint","venue":"Research Square","topic":"Scheduling and Timetabling Solutions","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Heuristics; Schedule; Computer science; Solver; Session (web analytics); Scheduling (production processes); Greedy algorithm; Mathematical optimization; Operations research; Algorithm; Programming language; Engineering; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow","scholarly_communication","research_integrity","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.02866615,0.0003380938,0.0007168259,0.002857789,0.001221509,0.002316998,0.001605581,0.0005278858,0.0004240945],"category_scores_gemma":[0.02821713,0.000281503,0.0002994442,0.003284671,0.0003591138,0.0002244268,0.004270853,0.002585907,0.001819322],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00017731,"about_ca_system_score_gemma":0.0009312343,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001679539,"about_ca_topic_score_gemma":0.0001199882,"domain_scores_codex":[0.9892604,0.001780188,0.0009258197,0.00175997,0.005001784,0.001271811],"domain_scores_gemma":[0.9874952,0.007673122,0.0002427661,0.001991762,0.002082125,0.0005150185],"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":[0.0005788144,0.001665502,0.2133405,0.002733014,0.001979852,0.001472225,0.01287586,0.2920685,0.01142888,0.02075934,0.1642142,0.2768833],"study_design_scores_gemma":[0.0009483692,0.0001887214,0.06534683,0.002181437,0.0001428353,0.00005325254,0.004943539,0.5274977,0.0006872585,0.3646317,0.03197328,0.001405077],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.96533,0.008602737,0.01463497,0.003126704,0.003006412,0.0013477,0.0003344486,0.000570226,0.003046803],"genre_scores_gemma":[0.9671745,0.0004502302,0.01797395,0.00003216988,0.001032725,0.00008261502,0.00005414134,0.0001091997,0.01309043],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3438724,"threshold_uncertainty_score":0.9999637,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.7041909162899727,"score_gpt":0.5871448906765433,"score_spread":0.1170460256134294,"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."}}