{"id":"W3186929351","doi":"10.48550/arxiv.2107.12524","title":"Ensemble Learning For Mega Man Level Generation","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Artificial Intelligence in Games","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Variance (accounting); Markov chain; Computer science; Mega-; Ensemble learning; Content (measure theory); Similarity (geometry); Artificial intelligence; Process (computing); Machine learning; Mathematics; Image (mathematics); 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002782544,0.0002450811,0.000261981,0.0001710017,0.0002901978,0.0003398402,0.001218815,0.0002668287,0.00003136579],"category_scores_gemma":[0.0001277759,0.0003127472,0.0002307393,0.0003337179,0.00006012298,0.0004598745,0.001283254,0.0004496209,0.00004045695],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001904267,"about_ca_system_score_gemma":0.0002452969,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001528642,"about_ca_topic_score_gemma":0.0002574743,"domain_scores_codex":[0.9981316,0.0001374593,0.0002126913,0.001091064,0.00009235249,0.0003347888],"domain_scores_gemma":[0.9983806,0.0001365056,0.0002135101,0.0008130813,0.0003499368,0.0001063575],"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":[0.000008663835,0.00005551873,0.0005576737,0.00004751962,0.00006319446,0.0001084473,0.0008040406,0.8910611,0.002012669,0.09678498,0.000536878,0.007959282],"study_design_scores_gemma":[0.00007506195,0.0000434984,0.00008600851,0.00003980639,0.00003200466,0.000002869844,0.0002249051,0.9671619,0.01733493,0.01325846,0.00138516,0.0003554286],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2018205,0.00004713453,0.7960008,0.0001251283,0.0008100575,0.0002537671,0.000002897646,0.0001640681,0.0007756069],"genre_scores_gemma":[0.975834,0.00007179736,0.01816574,0.000104018,0.0002110335,0.000003448177,0.00003938085,0.00001939436,0.005551205],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7778351,"threshold_uncertainty_score":0.9999325,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2615583772019597,"score_gpt":0.2386729315705764,"score_spread":0.02288544563138328,"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."}}