{"id":"W2663077448","doi":"10.20965/jaciii.2009.p0511","title":"Special Issue on Soft Computing for Modeling and Simulation","year":2009,"lang":"en","type":"article","venue":"Journal of Advanced Computational Intelligence and Intelligent Informatics","topic":"AI-based Problem Solving and Planning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Soft computing; Excellence; Informatics; Soft skills; Event (particle physics); Government (linguistics); Modeling and simulation; Software; Software engineering; Engineering management; Data science; Artificial intelligence; Simulation; Management; Fuzzy logic; Engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003565684,0.001998817,0.002512141,0.003040652,0.001925125,0.009031454,0.002440821,0.004471557,0.1587942],"category_scores_gemma":[0.01048132,0.0007506533,0.002737739,0.002149732,0.001632525,0.006210732,0.002976321,0.008293057,0.04877236],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002061319,"about_ca_system_score_gemma":0.003146298,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001070541,"about_ca_topic_score_gemma":0.001450815,"domain_scores_codex":[0.9964934,0.0006875856,0.0003152268,0.0005075862,0.001753112,0.0002430294],"domain_scores_gemma":[0.9908978,0.003941421,0.0004067891,0.0008353252,0.002483095,0.001435566],"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":[0.00002247148,0.00003161105,0.00008770545,0.0003050642,0.00002584254,0.00005192145,0.00003923354,0.0003649195,0.0001735171,0.01057277,0.9571127,0.03121225],"study_design_scores_gemma":[0.000008985769,0.00003167818,0.00013789,0.0002148691,0.00001059578,0.00008060805,0.00002955751,0.0007055494,0.00008337894,0.008909339,0.9897776,0.000009748055],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"editorial","genre_gemma":"editorial","genre_scores_codex":[0.0005354118,0.05623293,0.01448109,0.05263736,0.7986234,0.0002342597,0.0009738869,0.0009141904,0.07536731],"genre_scores_gemma":[0.007147755,0.06214254,0.007972308,0.01506342,0.6224661,0.0003897725,0.001965136,0.002049571,0.2808034],"genre_candidate":"editorial","genre_consensus":"editorial","teacher_disagreement_score":0.1587942,"threshold_uncertainty_score":0.5312195,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03538936970135051,"score_gpt":0.3191531456959236,"score_spread":0.283763775994573,"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."}}