{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001687612,0.001102253,0.001122648,0.001066452,0.001023759,0.002065399,0.002089221,0.001200434,0.004228197],"category_scores_gemma":[0.009167582,0.001124418,0.001500653,0.0005782306,0.001607002,0.002406789,0.005130688,0.002420638,0.001061536],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001105341,"about_ca_system_score_gemma":0.001092329,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003565447,"about_ca_topic_score_gemma":0.008702693,"domain_scores_codex":[0.9988727,0.000324363,0.0001045407,0.0001862814,0.0003951435,0.0001170164],"domain_scores_gemma":[0.9967386,0.001465695,0.000254965,0.00104181,0.0003117115,0.0001872006],"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":[0.0007640612,0.0002614311,0.00795619,0.0007244239,0.0001923376,0.0006669586,0.004751708,0.5151921,0.0942821,0.144876,0.006235563,0.2240971],"study_design_scores_gemma":[0.00004894904,0.00003866103,0.0003113327,0.00004037532,0.00002151665,0.00008308982,0.000145297,0.9462219,0.009560229,0.03407057,0.009425201,0.00003283592],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01660373,0.0001244025,0.9792207,0.0001093374,0.00002307788,0.00008422737,0.00007030544,0.00216835,0.001595945],"genre_scores_gemma":[0.2008696,0.0001599257,0.7963537,0.00006898678,0.00001303473,0.0001497838,0.0002911206,0.0008564163,0.001237503],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004228197,"threshold_uncertainty_score":0.01414478,"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."}}