{"id":"W4205252725","doi":"10.1109/cog52621.2021.9619021","title":"StABLE: Analyzing Player Movement Similarity Using Text Mining","year":2021,"lang":"en","type":"article","venue":"2021 IEEE Conference on Games (CoG)","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Computer science; Similarity (geometry); String (physics); Sequence (biology); Data mining; Sequential Pattern Mining; Information retrieval; Data science; Artificial intelligence; Mathematics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003042745,0.0002478253,0.0003201296,0.0001568396,0.0001955458,0.0008473726,0.000674831,0.00009196791,0.0008900104],"category_scores_gemma":[0.0001408655,0.0002546134,0.00009234127,0.0008961919,0.00004867616,0.0005484655,0.0003191895,0.0002045924,0.00008008804],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006954918,"about_ca_system_score_gemma":0.0005051123,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003101007,"about_ca_topic_score_gemma":0.00005899717,"domain_scores_codex":[0.9977986,0.0001492063,0.0003981659,0.0007218518,0.0004900785,0.0004420975],"domain_scores_gemma":[0.9982967,0.00009707508,0.000173215,0.0008562001,0.0003904204,0.0001863275],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004665468,0.001906383,0.01337576,0.0003434808,0.001033292,0.001339418,0.006159284,0.02686836,0.1187988,0.5431978,0.03241576,0.2545151],"study_design_scores_gemma":[0.0004410355,0.0000544916,0.0005639255,0.0002409584,0.00005374869,0.000006617333,0.0005097309,0.9673136,0.02220818,0.001125739,0.007034786,0.0004471377],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03060522,0.0002002711,0.9583285,0.001128205,0.0008658636,0.0001589626,0.00006032789,0.0001218758,0.008530743],"genre_scores_gemma":[0.9527172,0.000245374,0.03769599,0.004366171,0.000223588,0.00001143865,0.00007631019,0.00003309336,0.004630817],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9404453,"threshold_uncertainty_score":0.9999906,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08269248880411519,"score_gpt":0.3274261537405421,"score_spread":0.2447336649364269,"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."}}