{"id":"W4397026545","doi":"10.1109/tg.2024.3402626","title":"AstroBug: Automatic Game Bug Detection Using Deep Learning","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Games","topic":"Digital Games and Media","field":"Social Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Computer science; Deep learning; Artificial intelligence; Game based learning; Natural language processing; Multimedia","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":[0.0002283654,0.0001222352,0.0001264699,0.0001918583,0.0001976536,0.0003145414,0.00009420729,0.00009404986,0.0003386643],"category_scores_gemma":[0.00002126381,0.0001206397,0.0001470187,0.0004413449,0.0001383496,0.0004105764,6.587653e-7,0.0002995774,0.0001988349],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001776872,"about_ca_system_score_gemma":0.00009917364,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000196701,"about_ca_topic_score_gemma":0.0008239021,"domain_scores_codex":[0.9988853,0.00009032581,0.0001669883,0.0002321254,0.0003147968,0.0003105139],"domain_scores_gemma":[0.9995613,0.0001483114,0.00002978001,0.00009510901,0.0000342763,0.0001312504],"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":[0.000004882677,0.00003671053,0.000005648297,0.0000300425,0.00002954845,0.000008951029,0.006361213,0.009426057,0.001011846,0.00008909494,0.000007327524,0.9829887],"study_design_scores_gemma":[0.0004871378,0.0004414762,0.0002789692,0.0006078018,0.0003117074,0.00004362049,0.0164646,0.3099579,0.0156875,0.0006044776,0.6542804,0.0008343996],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3549538,0.0003780831,0.6180072,0.0002928843,0.003457383,0.000268454,0.000007213908,0.00115286,0.02148209],"genre_scores_gemma":[0.9866377,0.00008701497,0.000366075,0.00003939112,0.0001847897,0.00001987455,5.045415e-7,0.00002486856,0.0126398],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9821543,"threshold_uncertainty_score":0.4919544,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02104470258763144,"score_gpt":0.2929950118440005,"score_spread":0.2719503092563691,"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."}}