{"id":"W2021984249","doi":"10.1142/s0129626407003034","title":"COMPARING MINIMUM NEIGHBORHOOD EVALUATION SCHEMES FOR FINDING SPATIALLY ROBUST SOLUTIONS","year":2007,"lang":"en","type":"article","venue":"Parallel Processing Letters","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"National Science Council","keywords":"Robustness (evolution); Fitness function; Mathematical optimization; Neighbourhood (mathematics); Computer science; Algorithm; Mathematics; Genetic algorithm","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001166185,0.0002349331,0.000225791,0.0002564326,0.0007150995,0.0002979618,0.0005552707,0.00007148706,0.000006286121],"category_scores_gemma":[0.0002807463,0.0002553583,0.0000812293,0.0005390343,0.00007866017,0.001270179,0.0001415686,0.0001670365,0.00001252787],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000279919,"about_ca_system_score_gemma":0.0001546485,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007063233,"about_ca_topic_score_gemma":0.00001525403,"domain_scores_codex":[0.9977692,0.00004964727,0.0004286759,0.0006048327,0.0004919549,0.0006556584],"domain_scores_gemma":[0.9986734,0.0001808428,0.0003199517,0.0003091632,0.0004028519,0.0001138038],"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":[0.00003824231,0.0001306389,0.001946136,0.00007593229,0.00004056804,0.000005047578,0.00150124,0.870895,0.005394638,0.002522692,0.0003332139,0.1171166],"study_design_scores_gemma":[0.001311179,0.00002427762,0.001409707,0.00006286675,0.00002317905,0.000007766935,0.00005594665,0.9951935,0.0007291564,0.0005733835,0.0002813841,0.0003276709],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002582567,0.0001871094,0.9936842,0.001705703,0.0003634246,0.0006505005,0.000001132587,0.0002846752,0.0005406549],"genre_scores_gemma":[0.4269929,0.000001668325,0.5721979,0.0005530455,0.0001253642,0.00006874315,0.00001571215,0.00001934038,0.00002535665],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4244103,"threshold_uncertainty_score":0.9999899,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08517098334736357,"score_gpt":0.3201502356648206,"score_spread":0.234979252317457,"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."}}