{"id":"W4387410109","doi":"10.1145/3573382.3616030","title":"The Role of Generative AI in Games Research","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 Waterloo; University of Victoria; University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Generative grammar; Popularity; Computer science; Conversation; Generative model; Artificial intelligence; Data science; Multimedia; Sociology; Psychology; Communication; Social psychology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001373904,0.00003942072,0.00005950581,0.0001439734,0.0001092246,0.00008781023,0.0008981992,0.00002456446,0.00001146281],"category_scores_gemma":[0.0002573251,0.00002484557,0.00002057948,0.001330399,0.000166889,0.0001599297,0.0003886451,0.0001416842,0.0003300604],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001893749,"about_ca_system_score_gemma":0.00007027589,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002929971,"about_ca_topic_score_gemma":0.0006152056,"domain_scores_codex":[0.9989129,0.0001652582,0.0001680024,0.0001653057,0.0003276995,0.0002608671],"domain_scores_gemma":[0.9987148,0.0007216261,0.00001778219,0.0003684209,0.0001531508,0.0000242407],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000002586277,0.00001547247,0.001763632,9.93675e-7,0.000003229208,0.000003954517,0.00382367,0.0004060139,0.009541433,0.7336572,0.00488909,0.2458927],"study_design_scores_gemma":[0.00001192591,0.00003864203,0.001878534,0.00000591051,1.407259e-7,3.66014e-7,0.002355496,0.2893918,0.3559458,0.3367631,0.01356341,0.00004486683],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7052351,0.001301163,0.09172523,0.05768407,0.0009155821,0.0009678331,0.000001791272,0.000572073,0.1415971],"genre_scores_gemma":[0.9954736,0.00006774479,0.001648919,0.00008192741,0.00002811841,0.00002239203,1.348213e-7,0.000003048829,0.002674141],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3968941,"threshold_uncertainty_score":0.4242369,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1060698708643391,"score_gpt":0.4122718054347272,"score_spread":0.3062019345703882,"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."}}