{"id":"W2088999732","doi":"10.1080/15732470500254535","title":"A modified shuffled frog-leaping optimization algorithm: applications to project management","year":2006,"lang":"en","type":"article","venue":"Structure and Infrastructure Engineering","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":188,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Benchmark (surveying); Computer science; Algorithm; Particle swarm optimization; Flowchart; Range (aeronautics); Mathematical optimization; USable; Domain (mathematical analysis); Evolutionary algorithm; Mathematics; Artificial intelligence; Engineering","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":[0.0001074153,0.0002799862,0.0002355058,0.0005220392,0.0001703997,0.0003562409,0.0005549251,0.0001078457,0.00002936715],"category_scores_gemma":[0.00002117016,0.0002654628,0.00003914536,0.001167017,0.00002192983,0.000343997,0.0003132399,0.0002313231,0.000002155819],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007403178,"about_ca_system_score_gemma":0.00003644879,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001863751,"about_ca_topic_score_gemma":7.804067e-7,"domain_scores_codex":[0.9982467,0.00002692672,0.0003339351,0.0005834267,0.0003974996,0.0004114745],"domain_scores_gemma":[0.9991403,0.00003608954,0.00006730676,0.000500452,0.0001229739,0.000132844],"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":[0.000001931114,0.000005175227,0.00001470447,0.00005033754,0.00002574635,0.000005411085,0.00008602851,0.9426198,0.0003071995,0.01721502,0.0004755084,0.03919314],"study_design_scores_gemma":[0.000336771,0.0000215502,0.0009392714,0.00001768857,0.00001267526,0.00002784611,0.00001239863,0.9897929,0.0003428632,0.001285083,0.00691495,0.0002959492],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0003468082,0.0001338456,0.9973068,0.0001097514,0.0002127746,0.0009217876,0.00002378415,0.0002937705,0.0006506654],"genre_scores_gemma":[0.04024265,0.00002301925,0.9590104,0.00009432113,0.0002543115,0.0001220602,0.00005498205,0.00003069731,0.0001675348],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.04717315,"threshold_uncertainty_score":0.9999797,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005988490860227859,"score_gpt":0.2329618345007944,"score_spread":0.2269733436405665,"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."}}