{"id":"W2109648960","doi":"10.1145/2598784.2602795","title":"The consumed endurance workbench","year":2014,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"","keywords":"Workbench; Metric (unit); Computer science; Tracking (education); Human–computer interaction; Simulation; Engineering drawing; Artificial intelligence; Engineering; Visualization","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":[0.000575915,0.001149353,0.0005541772,0.001372904,0.0003165395,0.00114619,0.001079201,0.000683688,0.01598646],"category_scores_gemma":[0.005197266,0.0003822795,0.0004136171,0.0005732764,0.0002884378,0.001033028,0.001402971,0.0004535887,0.003411351],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00022832,"about_ca_system_score_gemma":0.0003312729,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001349025,"about_ca_topic_score_gemma":0.001950848,"domain_scores_codex":[0.9994647,0.00007226607,0.00006457817,0.0001054082,0.0002518601,0.00004124942],"domain_scores_gemma":[0.9983657,0.0006652008,0.0001677792,0.0002219419,0.0004193278,0.0001600785],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00302577,0.0004654746,0.03170706,0.002415602,0.0002298768,0.0009486267,0.002110113,0.01793593,0.1458416,0.009124198,0.06088809,0.7253076],"study_design_scores_gemma":[0.0004722939,0.003607084,0.165382,0.0008052453,0.0003291767,0.003491146,0.001620827,0.3011444,0.2436338,0.01291528,0.2658151,0.0007836276],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2630936,0.00115759,0.6214422,0.0003020036,0.0003984038,0.001630865,0.01574986,0.05821408,0.03801144],"genre_scores_gemma":[0.6994382,0.0005525078,0.2510391,0.0002916486,0.00007806949,0.001863167,0.01165905,0.005833027,0.02924518],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01598646,"threshold_uncertainty_score":0.05347997,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00989121410915561,"score_gpt":0.2164671241892032,"score_spread":0.2065759100800476,"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."}}