{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003764944,0.00010393,0.0001154939,0.0002355114,0.0001371669,0.000224123,0.0006117451,0.00005008506,0.00006860567],"category_scores_gemma":[0.000190511,0.0001008546,0.0000600657,0.001248985,0.00004611171,0.0007530407,0.0003608013,0.00009763319,0.001070564],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005693151,"about_ca_system_score_gemma":0.0000622227,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004302168,"about_ca_topic_score_gemma":0.00004452528,"domain_scores_codex":[0.998605,0.00006049056,0.0002578742,0.0004149821,0.0003223203,0.0003393354],"domain_scores_gemma":[0.9991231,0.0001856865,0.00007198929,0.0004722633,0.00008000427,0.00006698396],"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":[0.0000139574,0.00007520094,0.009776811,0.00003052261,0.00005649719,0.000142084,0.01014392,0.04993808,0.1252122,0.2541522,0.004542883,0.5459156],"study_design_scores_gemma":[0.00004212302,0.00001674546,0.002669027,0.00001976045,0.000002983843,0.00001210755,0.0005626853,0.8734096,0.09640572,0.02618715,0.0005003771,0.0001716909],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1403415,0.00000720875,0.8547874,0.0008656618,0.0004708752,0.0001026293,0.000001449846,0.0005758955,0.002847407],"genre_scores_gemma":[0.9291765,0.000009909565,0.06445586,0.0001583577,0.00009117477,0.000006688477,0.000002524474,0.0000125241,0.006086476],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8234715,"threshold_uncertainty_score":0.9997072,"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."}}