{"id":"W7046446402","doi":"","title":"Data collection using deep reinforcement learning for serious games","year":2023,"lang":"en","type":"dissertation","venue":"Mspace (University of Manitoba)","topic":"Superconducting and THz Device Technology","field":"Physics and Astronomy","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"","keywords":"Reinforcement learning; Video game; Stimulus (psychology); Reinforcement; Data collection; Action (physics); Deep learning; Artificial neural network; Serious game","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":[0.002363092,0.001047819,0.0006079502,0.001279783,0.0004731105,0.0008618844,0.001590687,0.0009253633,0.003667545],"category_scores_gemma":[0.01350185,0.0004022139,0.0007615159,0.0008582727,0.0006188172,0.0007919352,0.001565617,0.001735596,0.002636849],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008892374,"about_ca_system_score_gemma":0.001103329,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00616386,"about_ca_topic_score_gemma":0.009135202,"domain_scores_codex":[0.9983418,0.0005107803,0.0001827719,0.0004442822,0.0003984548,0.0001218897],"domain_scores_gemma":[0.9962423,0.001279988,0.0002827974,0.0007334275,0.001177729,0.0002837402],"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":[0.002027034,0.00242476,0.04929729,0.001272998,0.0004024706,0.000999594,0.0009834344,0.2453649,0.02623774,0.00894665,0.08621366,0.5758294],"study_design_scores_gemma":[0.000266585,0.0011068,0.02842189,0.0002077186,0.00005367309,0.0003156999,0.0005134573,0.880089,0.02356258,0.01880514,0.04651873,0.0001387203],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3121471,0.00121888,0.5811791,0.001854468,0.0008947271,0.005885939,0.06396591,0.02243512,0.01041877],"genre_scores_gemma":[0.5477484,0.0003442547,0.3841172,0.0005265424,0.00006993191,0.00448043,0.05695577,0.0005259318,0.005231556],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00616386,"threshold_uncertainty_score":0.01249737,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03675165984228802,"score_gpt":0.2592384738290471,"score_spread":0.2224868139867591,"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."}}