{"id":"W2112394655","doi":"10.1287/opre.1110.0951","title":"An Interior Point Constraint Generation Algorithm for Semi-Infinite Optimization with Health-Care Application","year":2011,"lang":"en","type":"article","venue":"Operations Research","topic":"Advanced Optimization Algorithms Research","field":"Mathematics","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Mathematical optimization; Computer science; Algorithm; Interior point method; Point (geometry); Constraint (computer-aided design); Linear programming; Branch and bound; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001566975,0.0002339115,0.0002840946,0.0005450599,0.001192706,0.0002743933,0.0003909325,0.000143235,0.0004158877],"category_scores_gemma":[0.0003520097,0.0002122964,0.00005297518,0.0008545515,0.0002952161,0.0008344809,0.00007727783,0.0004018003,0.00002913266],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004870984,"about_ca_system_score_gemma":0.0007446797,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000136979,"about_ca_topic_score_gemma":0.0005733318,"domain_scores_codex":[0.9968152,0.0004886962,0.0005968571,0.0006848814,0.000773339,0.0006410136],"domain_scores_gemma":[0.995301,0.0001870731,0.00008503192,0.0008179356,0.003293929,0.0003150433],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002260637,0.001345411,0.00004362304,0.0003367322,0.0001783241,0.000009812711,0.0325089,0.2907902,0.004873705,0.06433777,0.002204962,0.6031445],"study_design_scores_gemma":[0.0009358244,0.001006653,0.000004972116,0.00003560799,0.000008500396,0.00001586539,0.005145481,0.9864129,0.005371665,0.0003836933,0.0004319888,0.0002467934],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.000728117,0.0000623764,0.9936991,0.0004960879,0.00005091467,0.003824767,0.0001593458,0.0001614231,0.0008178503],"genre_scores_gemma":[0.03807567,0.00006190068,0.95724,0.0001092293,0.0002278023,0.00271293,0.001120359,0.00009917453,0.0003529645],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.6956227,"threshold_uncertainty_score":0.917345,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1551299155174184,"score_gpt":0.4445563865709762,"score_spread":0.2894264710535578,"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."}}