{"id":"W4416771567","doi":"10.1609/aiide.v21i1.36952","title":"FrontMatter","year":2025,"lang":"","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment","topic":"Digital Games and Media","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Alberta Innovates; Alberta Machine Intelligence Institute","keywords":"Applications of artificial intelligence; Set (abstract data type); Natural (archaeology); Key (lock)","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0003159206,0.0004527082,0.0005278951,0.0002088921,0.0002299857,0.001460504,0.0009972651,0.0001645687,0.0008329258],"category_scores_gemma":[0.0006254727,0.000339279,0.0003306513,0.0004855685,0.001428293,0.001396535,0.0006476217,0.0004608237,0.0002472244],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002244017,"about_ca_system_score_gemma":0.0001778709,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006196812,"about_ca_topic_score_gemma":0.00002284977,"domain_scores_codex":[0.9970887,0.00002453287,0.0009104138,0.0007130801,0.0006613868,0.0006019422],"domain_scores_gemma":[0.9981934,0.0002100569,0.0005369678,0.0002053923,0.0006572651,0.000196915],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0005186469,0.0008061822,0.001115235,0.0001089733,0.0001884181,7.070896e-7,0.0115426,0.00000175229,0.0009605421,0.3749413,0.003126348,0.6066893],"study_design_scores_gemma":[0.0003861533,0.002373545,0.001472281,0.0105944,0.0003515316,0.000005268826,0.2404799,0.003343614,0.2500045,0.1742187,0.3151388,0.001631281],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.1701209,0.00008773269,0.0002359042,0.01064151,0.001533081,0.000826924,0.00005970562,0.00002413494,0.8164701],"genre_scores_gemma":[0.8955367,0.000180043,0.00001216271,0.001142445,0.00009901989,0.00003993138,0.000001973781,0.00001505562,0.1029727],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7254158,"threshold_uncertainty_score":0.9999059,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0365538546710614,"score_gpt":0.3106547302440802,"score_spread":0.2741008755730189,"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."}}