{"id":"W644136860","doi":"10.1017/cbo9781107282094","title":"A Gentle Introduction to Optimization","year":2014,"lang":"en","type":"book","venue":"Cambridge University Press eBooks","topic":"Advanced Optimization Algorithms Research","field":"Mathematics","cited_by":55,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Reading (process); Section (typography); Focus (optics); Point (geometry); Selection (genetic algorithm); Computer science; Mathematics education; Range (aeronautics); Management science; Engineering; Mathematics; Artificial intelligence; Linguistics","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":[0.0003397331,0.001566061,0.001066326,0.001341874,0.000871965,0.002870797,0.001158062,0.001319941,0.08555285],"category_scores_gemma":[0.001707339,0.0007402555,0.0009500482,0.001967583,0.00122673,0.003068331,0.001645136,0.004077923,0.06780718],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009215574,"about_ca_system_score_gemma":0.001003031,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007060216,"about_ca_topic_score_gemma":0.001211313,"domain_scores_codex":[0.9995121,0.00006062034,0.00002358013,0.00009730044,0.0002730337,0.00003342844],"domain_scores_gemma":[0.9995342,0.0002231595,0.0000240783,0.00005735474,0.0001225193,0.00003868851],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002206662,0.00004609001,0.00008685734,0.0005280632,0.00001459121,0.0001133321,0.0001840504,0.002685598,0.001166799,0.1738794,0.640952,0.180321],"study_design_scores_gemma":[0.000002901376,0.00001426968,0.0001221686,0.0001526649,0.000002749397,0.0001542232,0.00002021279,0.0006284082,0.0001099433,0.0397072,0.9590782,0.000006978469],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"methods","genre_scores_codex":[0.000975697,0.05510873,0.09335654,0.008146545,0.007106875,0.0001560471,0.001415216,0.002242935,0.8314915],"genre_scores_gemma":[0.01087353,0.05200197,0.06118786,0.005082341,0.005594397,0.0002583683,0.001589759,0.001761383,0.8616505],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.08555285,"threshold_uncertainty_score":0.2862028,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03125914174418862,"score_gpt":0.2665371823380287,"score_spread":0.2352780405938401,"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."}}