{"id":"W4389315259","doi":"10.1109/cog57401.2023.10333227","title":"Game Level Blending using a Learned Level Representation","year":2023,"lang":"en","type":"article","venue":"","topic":"Artificial Intelligence in Games","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Representation (politics); Annotation; Cluster analysis; Artificial intelligence; Popularity; Process (computing); Machine learning; Human–computer interaction; Programming language","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":[0.0003926757,0.001207899,0.0006851227,0.00133307,0.0002892215,0.00145407,0.001022947,0.0007541735,0.005402817],"category_scores_gemma":[0.002413512,0.0004366875,0.00105375,0.0008239355,0.0005885992,0.002040862,0.001970891,0.001169751,0.001564233],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007166417,"about_ca_system_score_gemma":0.0004701902,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002313536,"about_ca_topic_score_gemma":0.004289208,"domain_scores_codex":[0.9994791,0.0000843119,0.00002814507,0.000220616,0.0001345501,0.0000533679],"domain_scores_gemma":[0.9995587,0.0001240716,0.00005737935,0.0001301063,0.00008040017,0.00004924527],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006689926,0.0005072749,0.005337672,0.0003300425,0.0001476127,0.0002708373,0.0007225469,0.1693772,0.06611123,0.02000678,0.008865676,0.7276541],"study_design_scores_gemma":[0.00003663853,0.0001694856,0.001231515,0.00003099609,0.00003309321,0.0001229771,0.0001866909,0.9661387,0.01396902,0.0119035,0.006143897,0.00003349502],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03302481,0.0001107346,0.9573123,0.00008044618,0.00006257147,0.0001825064,0.0003610831,0.005428456,0.003437132],"genre_scores_gemma":[0.3936383,0.0001685848,0.5967907,0.0001333209,0.00001951045,0.0003054963,0.001847203,0.0007729008,0.006323999],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005402817,"threshold_uncertainty_score":0.01807415,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4959494911009171,"score_gpt":0.4310482392168456,"score_spread":0.06490125188407153,"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."}}