{"id":"W4312371681","doi":"10.1609/aiide.v10i2.12738","title":"Preface","year":2014,"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":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Presentation (obstetrics); Pleasure; Computer science; Adversarial system; Representation (politics); Term (time); Artificial intelligence; Psychology; Political science","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":[],"consensus_categories":[],"category_scores_codex":[0.0002926275,0.0003026883,0.0002979687,0.0001323742,0.0001706688,0.0007755699,0.001547587,0.00007271326,0.00004257877],"category_scores_gemma":[0.000551913,0.0002165286,0.0001512803,0.0002960619,0.000363354,0.001485581,0.0007337404,0.0002915082,0.000124175],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007124171,"about_ca_system_score_gemma":0.00003108632,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000176706,"about_ca_topic_score_gemma":0.000003462874,"domain_scores_codex":[0.997868,0.000021288,0.0006063664,0.0006180878,0.0005058881,0.0003803528],"domain_scores_gemma":[0.9985004,0.0002071131,0.0003876214,0.0003370679,0.0004357173,0.0001320925],"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":[0.00006869545,0.0002211265,0.000433476,0.00001807749,0.00002686362,2.518881e-7,0.001871075,0.00002432855,0.00640709,0.7039887,0.0001042363,0.286836],"study_design_scores_gemma":[0.0000345306,0.0008177311,0.0002912171,0.0003684491,0.00001176359,0.00001037986,0.002229081,0.0764792,0.683433,0.23392,0.002019733,0.0003849261],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6197287,0.00003422677,0.2314912,0.01008896,0.001291042,0.001172161,0.00001761678,0.0002318914,0.1359442],"genre_scores_gemma":[0.9983166,0.00002249587,0.0005454594,0.0004330075,0.00005673731,0.00003306505,6.224902e-7,0.0000137518,0.0005781972],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6770259,"threshold_uncertainty_score":0.8829779,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03891820528764053,"score_gpt":0.2792691293390521,"score_spread":0.2403509240514116,"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."}}