{"id":"W4414463589","doi":"10.1109/acdsa65407.2025.11166048","title":"Serious Game Scoring for Maze Resolution","year":2025,"lang":"en","type":"article","venue":"","topic":"Face recognition and analysis","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Public Health","funders":"","keywords":"Serious game; Measure (data warehouse); Resolution (logic); Work (physics); Game theory; Screening game; Key (lock); Scheme (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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00422722,0.00158818,0.0009852913,0.00249354,0.001321745,0.003853344,0.002910005,0.001409703,0.03676701],"category_scores_gemma":[0.03497767,0.0006104576,0.0009797235,0.002057914,0.001509277,0.003070209,0.004131022,0.002453157,0.008734007],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001312211,"about_ca_system_score_gemma":0.00196191,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004267977,"about_ca_topic_score_gemma":0.007532997,"domain_scores_codex":[0.993471,0.002301734,0.00048611,0.0008002829,0.002620246,0.0003205422],"domain_scores_gemma":[0.9887687,0.004732423,0.00111582,0.002128535,0.002685951,0.0005686723],"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":[0.0003403803,0.0002971079,0.004249944,0.0004278992,0.00009110691,0.0002128124,0.0006655536,0.04736634,0.008206683,0.2086761,0.05297259,0.6764935],"study_design_scores_gemma":[0.0000809036,0.0004039274,0.004996059,0.0003781455,0.00003754524,0.0006150247,0.0004332839,0.6395951,0.01070688,0.2039624,0.1386005,0.0001902681],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006080447,0.0002669379,0.9644784,0.0003049594,0.0002276169,0.0004156226,0.0005534997,0.006875965,0.02079654],"genre_scores_gemma":[0.1099667,0.0002372199,0.8754575,0.0001933807,0.0000898792,0.0006612368,0.001238859,0.001517965,0.01063721],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03676701,"threshold_uncertainty_score":0.1229979,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01298726388434252,"score_gpt":0.2680788156377162,"score_spread":0.2550915517533737,"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."}}