{"id":"W90200542","doi":"10.1007/978-3-642-28783-1_21","title":"A Two-Stage Corrective Markov Model for Activities of Daily Living Detection","year":2012,"lang":"en","type":"book-chapter","venue":"Advances in intelligent and soft computing","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Hidden Markov model; Stage (stratigraphy); Computer science; Markov model; Artificial intelligence; Markov chain; Markov process; Segmentation; Domain (mathematical analysis); Pattern recognition (psychology); Machine learning; Data mining; Mathematics; Statistics","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":[0.002013553,0.0009734607,0.002034563,0.0007549116,0.0006201897,0.0008852653,0.003583536,0.001863117,0.003865421],"category_scores_gemma":[0.003776013,0.0008704661,0.001415259,0.0008630144,0.0005924038,0.001388882,0.001308983,0.002536645,0.001534011],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00110508,"about_ca_system_score_gemma":0.0016657,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02393531,"about_ca_topic_score_gemma":0.02748389,"domain_scores_codex":[0.999168,0.000222589,0.00005623016,0.0002824251,0.0001379232,0.0001328509],"domain_scores_gemma":[0.9976286,0.001756252,0.0001011232,0.0001436388,0.0003102677,0.00006013919],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0009043824,0.0002866776,0.003393304,0.0002261278,0.0002172894,0.0001951872,0.0001748966,0.7246903,0.004599809,0.02254019,0.004819623,0.2379523],"study_design_scores_gemma":[0.000007179756,0.00001983677,0.0002153424,0.000005044401,0.00001714252,0.00002073634,0.000003214258,0.9965526,0.0002667215,0.002684292,0.0001983362,0.000009559411],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01527761,0.0006096403,0.9814309,0.0002601471,0.0001284116,0.00006238097,0.0004221751,0.0009749008,0.0008339381],"genre_scores_gemma":[0.7583368,0.0008994304,0.2259346,0.0004039137,0.0002362276,0.0005786431,0.002250513,0.000231059,0.01112879],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02393531,"threshold_uncertainty_score":0.04759198,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03150297612552224,"score_gpt":0.2856761116245793,"score_spread":0.2541731354990571,"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."}}