{"id":"W4387394641","doi":"10.1609/aiide.v19i1.27523","title":"Reconstructing Existing Levels through Level Inpainting","year":2023,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment","topic":"Video Analysis and Summarization","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Alberta Machine Intelligence Institute; Natural Sciences and Engineering Research Council of Canada; Canadian Institute for Advanced Research","keywords":"Inpainting; Autoencoder; Artificial intelligence; Computer science; Task (project management); Domain (mathematical analysis); Image (mathematics); Baseline (sea); Deep learning; Computer vision; Machine learning; Mathematics; Engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0004704727,0.0009683943,0.0005922305,0.0008068329,0.0003023083,0.001088724,0.0009064949,0.0007734623,0.003244682],"category_scores_gemma":[0.001638315,0.0004548201,0.0007356009,0.0004123554,0.0006236174,0.001052837,0.00148343,0.001100844,0.0009940289],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003319214,"about_ca_system_score_gemma":0.000401074,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008341383,"about_ca_topic_score_gemma":0.001965199,"domain_scores_codex":[0.9996884,0.00004149411,0.00001536775,0.00008805726,0.0001213556,0.00004542432],"domain_scores_gemma":[0.999267,0.0002764637,0.00006612073,0.0002383085,0.0001081789,0.00004388733],"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":[0.0003089621,0.0001748217,0.001508501,0.0003827217,0.00008115307,0.0005880463,0.0006277566,0.1801138,0.2095132,0.01729959,0.006124537,0.5832769],"study_design_scores_gemma":[0.00004010051,0.0002390116,0.001075204,0.00004697888,0.00005493576,0.0005784218,0.0001746126,0.8527336,0.1156379,0.01509877,0.01428221,0.00003830847],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02080139,0.0001309956,0.9753574,0.00008030319,0.00004705413,0.0001126446,0.00008785455,0.001268314,0.002113992],"genre_scores_gemma":[0.2311856,0.0002332986,0.7637028,0.000130304,0.00004084906,0.0000945389,0.000377408,0.0004596166,0.003775617],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003244682,"threshold_uncertainty_score":0.01085448,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.178134087742348,"score_gpt":0.3272380837486853,"score_spread":0.1491039960063373,"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."}}