{"id":"W1542369266","doi":"10.1007/11740698_13","title":"The Importance of Scalability When Comparing Dynamic Weighted Aggregation and Pareto Front Techniques","year":2006,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Scalability; Computer science; Computation; Multi-objective optimization; Genetic algorithm; Population; Pareto principle; Mathematical optimization; Algorithm; Machine learning; Mathematics; Database","routes":{"ca_aff":true,"ca_fund":true,"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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007768378,0.0004147643,0.0005006127,0.0003339592,0.0003613526,0.0002822429,0.001761483,0.0002059985,0.000002670772],"category_scores_gemma":[0.0001122457,0.0003255164,0.00007515957,0.000326777,0.001372922,0.0006292242,0.0009981927,0.0005048053,0.000001656302],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004417713,"about_ca_system_score_gemma":0.000231714,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003540129,"about_ca_topic_score_gemma":0.0005377485,"domain_scores_codex":[0.9969022,0.0000523717,0.0006977255,0.001213065,0.0007137004,0.0004209501],"domain_scores_gemma":[0.9969962,0.0005583621,0.0006386303,0.001228502,0.0004910519,0.00008730552],"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":[0.00001612786,0.00003699183,0.002317316,0.00004781959,0.00001357777,0.00001257238,0.0005642697,0.01894572,0.0000746318,0.006176735,0.00000988671,0.9717844],"study_design_scores_gemma":[0.0001684258,0.000071582,0.001189614,0.0002115669,0.000004911911,0.0000173303,2.284741e-7,0.8351943,0.001845193,0.1607874,0.000177434,0.0003320325],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0002331018,0.0009033398,0.99648,0.0003315218,0.0003771916,0.0006313053,0.000004536165,0.0001686295,0.0008702998],"genre_scores_gemma":[0.07813392,0.00009580242,0.9214009,0.000136769,0.00006509914,0.00001830607,0.000006938006,0.00002475433,0.0001175201],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9714523,"threshold_uncertainty_score":0.9999197,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008861077321542761,"score_gpt":0.2437161917820587,"score_spread":0.2348551144605159,"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."}}