{"id":"W3094114563","doi":"10.1609/aiide.v16i1.7429","title":"Image-to-Level: Generation and Repair","year":2020,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment","topic":"Artificial Intelligence in Games","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Machine Intelligence Institute","keywords":"Computer science; Process (computing); Fidelity; Image (mathematics); Human–computer interaction; Artificial intelligence; Game design; Content (measure theory); Multimedia; Programming language; Mathematics","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":[0.0009489037,0.0008612687,0.0005258342,0.0007935608,0.0004451872,0.001363862,0.002354827,0.001284008,0.01828437],"category_scores_gemma":[0.006926204,0.0004919947,0.0008168451,0.0003380627,0.0009891071,0.001445185,0.00279132,0.001011417,0.005093339],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005108271,"about_ca_system_score_gemma":0.0004763546,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001308432,"about_ca_topic_score_gemma":0.001181638,"domain_scores_codex":[0.9991707,0.00013953,0.00004888525,0.0001947428,0.0003580495,0.0000881674],"domain_scores_gemma":[0.9972147,0.001035098,0.0001723045,0.001085718,0.0003671969,0.0001250158],"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":[0.0008920469,0.0005481068,0.00369147,0.0007365668,0.00008183787,0.001030683,0.001709099,0.1007071,0.1100712,0.05163275,0.03001318,0.6988859],"study_design_scores_gemma":[0.0001165961,0.0003232258,0.001471163,0.00008021738,0.00004844641,0.0007376273,0.0002523477,0.7711681,0.1442475,0.02866117,0.0528,0.00009359402],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01756919,0.00007329358,0.9581575,0.0001464173,0.0001061429,0.000284385,0.0002868353,0.0163459,0.007030285],"genre_scores_gemma":[0.3196414,0.0001041579,0.6626944,0.000196962,0.00003841651,0.0003164768,0.001056194,0.004209149,0.01174276],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01828437,"threshold_uncertainty_score":0.06116736,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09519799346194492,"score_gpt":0.2999875914977209,"score_spread":0.204789598035776,"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."}}