{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003053309,0.0001516055,0.0002175895,0.00008217799,0.0002974817,0.0003295722,0.0002971068,0.00006710162,0.00001267614],"category_scores_gemma":[0.00008595127,0.0001502453,0.00006087443,0.0001725722,0.00003686619,0.0008324144,0.0002190626,0.0001062576,0.00006869632],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000423028,"about_ca_system_score_gemma":0.00004989898,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002525664,"about_ca_topic_score_gemma":0.000005317971,"domain_scores_codex":[0.9987527,0.00006363753,0.0002689197,0.0004611821,0.0001625859,0.0002909912],"domain_scores_gemma":[0.998555,0.0005587245,0.0001660388,0.0002676036,0.0003398301,0.0001127962],"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":[4.827663e-7,0.00002240244,0.0001423555,0.00003242064,0.00001093595,0.000001386098,0.001087124,0.0005997226,0.0003063582,0.0001167253,0.0007112957,0.9969688],"study_design_scores_gemma":[0.0003050506,0.0001009711,0.001300446,0.0000843907,0.000005999201,0.00006658362,0.0002135156,0.9954951,0.0005948744,0.0004644807,0.001176122,0.0001924476],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05926592,0.0002670259,0.9386069,0.0002570563,0.0002779279,0.0009872931,0.000004804756,0.0001717284,0.0001613114],"genre_scores_gemma":[0.7673854,0.00001325146,0.2317446,0.0002358279,0.0001650749,0.000250602,0.000009348206,0.00001238085,0.0001835322],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9967763,"threshold_uncertainty_score":0.6126825,"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."}}