{"id":"W1844242138","doi":"","title":"SAGA-ML: an active learning system for semi-automated gameplay analysis","year":2005,"lang":"en","type":"article","venue":"","topic":"Software Engineering Research","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Correctness; Set (abstract data type); Context (archaeology); Black box; Software; Artificial intelligence; Machine learning; Visualization; Human–computer interaction; Component (thermodynamics); Video game; Software engineering; Programming language; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002346756,0.001424019,0.001075352,0.001501646,0.0005667006,0.001907998,0.003915106,0.002188224,0.009640556],"category_scores_gemma":[0.01087967,0.0007499594,0.0007875102,0.0005586434,0.00108877,0.003088896,0.002567525,0.00266653,0.003931917],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005975817,"about_ca_system_score_gemma":0.000848417,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001460932,"about_ca_topic_score_gemma":0.002057513,"domain_scores_codex":[0.9984558,0.000659908,0.0001066355,0.0002952292,0.000389888,0.00009258225],"domain_scores_gemma":[0.9943312,0.003912151,0.0002895144,0.0006678301,0.0005733314,0.0002259245],"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":[0.00126102,0.0008670392,0.005273039,0.0006312201,0.0002768331,0.0003301218,0.0009735419,0.1465137,0.02755621,0.02059906,0.027263,0.7684551],"study_design_scores_gemma":[0.0000481518,0.00008570057,0.0003181597,0.00001900247,0.00001729327,0.00005520305,0.00002416397,0.97394,0.00852472,0.01187962,0.005062691,0.00002535042],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003613502,0.00004497763,0.9498114,0.00006604484,0.00002777863,0.00009911508,0.0002436984,0.04550002,0.0005934979],"genre_scores_gemma":[0.2002697,0.00008110181,0.7921087,0.0002054309,0.00004466659,0.0007568422,0.001255912,0.00265117,0.00262655],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009640556,"threshold_uncertainty_score":0.03225082,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01672153389873049,"score_gpt":0.2915047009522573,"score_spread":0.2747831670535268,"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."}}