{"id":"W2954024736","doi":"10.1609/aiide.v15i1.5219","title":"Automatic Abstraction and Refinement for Simulations with Adaptive Level of Detail","year":2019,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Scope (computer science); Context (archaeology); Abstraction; Graphics; Human–computer interaction; Computer graphics; Interactive simulation; Data science; Artificial intelligence; Simulation; Programming language; Computer graphics (images)","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":[],"consensus_categories":[],"category_scores_codex":[0.00008096341,0.0001211539,0.0001631589,0.00008072792,0.00005170362,0.0001900982,0.0002484821,0.00002330245,0.0000201866],"category_scores_gemma":[0.00007058847,0.00008211291,0.00003862721,0.0001207716,0.0000892976,0.0008091006,0.0001289009,0.00006312956,0.000003519595],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002861964,"about_ca_system_score_gemma":0.00002657176,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008565601,"about_ca_topic_score_gemma":0.000004579092,"domain_scores_codex":[0.9991235,0.000003922615,0.0003104511,0.0002426938,0.0002079555,0.0001114646],"domain_scores_gemma":[0.9990703,0.0001007555,0.0003200206,0.0001044042,0.0003663869,0.00003810122],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003109963,0.0005755941,0.001995928,0.0001657561,0.0001402183,1.288137e-7,0.003480677,0.0002663589,0.01165903,0.7883701,0.00004770015,0.1929875],"study_design_scores_gemma":[0.0001637072,0.001634056,0.001357712,0.000613467,0.00002840039,0.000003305386,0.004312912,0.7899433,0.1844938,0.01706518,0.0001737434,0.000210366],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8400277,0.000005213921,0.155862,0.0007941677,0.00009839498,0.0007355666,0.0001005912,0.00001926953,0.002356994],"genre_scores_gemma":[0.9987406,0.000007224234,0.0009531973,0.00007035401,0.00000575335,0.00001149305,0.000003672537,0.000004927193,0.0002027681],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.789677,"threshold_uncertainty_score":0.3348467,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1087768928764141,"score_gpt":0.3203551584741755,"score_spread":0.2115782655977614,"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."}}