{"id":"W4380609504","doi":"10.1139/cjce-2022-0485","title":"Adaptive selection slime mould algorithm in time–cost–quality–environmental impact trade-off optimization","year":2023,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Slime Mold and Myxomycetes Research","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Selection (genetic algorithm); Computer science; Mathematical optimization; Quality (philosophy); Range (aeronautics); Algorithm; Operations research; Engineering; Industrial engineering; Artificial intelligence; Mathematics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004083366,0.0001774777,0.0002431385,0.001036413,0.00004373552,0.00006239179,0.0001827864,0.0001168936,0.0004074046],"category_scores_gemma":[0.00004609746,0.0001937171,0.0001101304,0.0006820115,0.00001666973,0.0002915032,0.000009383388,0.0004734294,0.00003262587],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009433824,"about_ca_system_score_gemma":0.0001583843,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002423215,"about_ca_topic_score_gemma":0.002048695,"domain_scores_codex":[0.998694,0.00002956027,0.0003603989,0.0001084429,0.0002620438,0.0005455333],"domain_scores_gemma":[0.9992852,0.00006507781,0.00003774393,0.00008643671,0.00001672897,0.000508853],"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":[0.000003883192,0.000004419695,0.003014655,0.00001113784,0.00006112619,0.00009798447,0.0002045608,0.9851343,0.002470006,0.000002188761,0.00082965,0.008166104],"study_design_scores_gemma":[0.0004021436,0.00007192236,0.04549539,0.00006962026,0.000009394043,0.00008642374,0.00004595707,0.9521241,0.0005794148,0.000005839695,0.0009114387,0.0001983335],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9497395,0.003905442,0.04124448,0.0002754091,0.00156684,0.0009290969,0.0002826778,0.0004528416,0.001603726],"genre_scores_gemma":[0.9987973,0.0001397349,0.0007089301,0.000007813206,0.000190181,0.000006998126,0.00001817408,0.00006087877,0.00007000853],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0490578,"threshold_uncertainty_score":0.7899555,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01256772396255669,"score_gpt":0.229248512679705,"score_spread":0.2166807887171483,"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."}}