{"id":"W2069619006","doi":"10.1016/j.pmcj.2013.09.006","title":"RRACE: Robust realtime algorithm for cadence estimation","year":2013,"lang":"en","type":"article","venue":"Pervasive and Mobile Computing","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"University of British Columbia","keywords":"Cadence; Computer science; A priori and a posteriori; Accelerometer; Orientation (vector space); Algorithm; Real-time computing; Latency (audio); Robustness (evolution); Artificial intelligence; Telecommunications","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0009440585,0.001386968,0.001246542,0.00178465,0.0004623604,0.00124234,0.002460852,0.001624127,0.01467452],"category_scores_gemma":[0.003115302,0.0006604965,0.0007273943,0.001182004,0.0003821942,0.001459044,0.001343858,0.0016365,0.006333863],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004608279,"about_ca_system_score_gemma":0.00116206,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003972338,"about_ca_topic_score_gemma":0.006040602,"domain_scores_codex":[0.9989495,0.000118934,0.00005096424,0.0002948286,0.0004996929,0.00008610939],"domain_scores_gemma":[0.999326,0.000186973,0.00006512051,0.0001672295,0.000218559,0.00003606404],"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":[0.0002878292,0.00008515673,0.0003932924,0.0001198978,0.00005226006,0.00004854454,0.00004750171,0.02450169,0.02456305,0.003462675,0.01108037,0.9353576],"study_design_scores_gemma":[0.0001106534,0.000196984,0.000924466,0.0000390779,0.00003426099,0.0002819771,0.00004135353,0.9345524,0.03517462,0.003471914,0.02512165,0.00005067803],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001442628,0.0001759045,0.9938784,0.00003519109,0.00005893568,0.00003704838,0.00007612135,0.003726592,0.0005692542],"genre_scores_gemma":[0.04373517,0.0001744154,0.950426,0.0001030944,0.00005956593,0.0001511004,0.0005343024,0.0005487766,0.004267568],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01467452,"threshold_uncertainty_score":0.04909116,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02472203923105641,"score_gpt":0.2670118799789327,"score_spread":0.2422898407478763,"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."}}