{"id":"W2786623934","doi":"","title":"Large-scale decomposition strategies for collaborative operating room planning and scheduling","year":2017,"lang":"en","type":"dissertation","venue":"TSpace (University of Toronto)","topic":"Healthcare Operations and Scheduling Optimization","field":"Health Professions","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Scheduling (production processes); Scale (ratio); Computer science; Decomposition; Operations research; Industrial engineering; Operations management; Engineering; Geography; Cartography; Chemistry","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004994873,0.000207755,0.000453668,0.00005972501,0.004069989,0.00006251001,0.0001753798,0.0005321148,0.001042415],"category_scores_gemma":[0.00008217983,0.000256293,0.00006246992,0.00004010363,0.00003105987,0.0008916209,0.00003575154,0.000342485,0.000003819926],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002595455,"about_ca_system_score_gemma":0.001196677,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.01579695,"about_ca_topic_score_gemma":0.1635701,"domain_scores_codex":[0.9987071,0.0002039461,0.0002473544,0.0003605867,0.0001552298,0.0003258073],"domain_scores_gemma":[0.99793,0.000174663,0.0006271916,0.0002465634,0.0009055136,0.0001160884],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.001050599,0.0001489342,0.003375636,0.004131799,0.000294756,0.0000101475,0.953818,0.009270225,0.003248465,0.01059189,0.00119277,0.0128668],"study_design_scores_gemma":[0.00141222,0.0001946265,0.02673554,0.001669153,0.0001241333,3.792093e-7,0.924269,0.04408369,0.00002432491,0.00005826653,0.001102248,0.0003264594],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8014811,0.003597702,0.07154609,0.001036311,0.001233694,0.003005845,0.0004031513,0.0001340112,0.117562],"genre_scores_gemma":[0.8877792,0.0005847005,0.09652571,0.00004104449,0.0002094452,0.00001613039,0.002216699,0.00004551458,0.01258155],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1477731,"threshold_uncertainty_score":0.9999889,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04729283734518976,"score_gpt":0.4139404109865467,"score_spread":0.366647573641357,"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."}}