{"id":"W2019151722","doi":"10.4018/ijrat.2013010103","title":"Ambient Activity Recognition in Smart Environments for Cognitive Assistance","year":2013,"lang":"en","type":"article","venue":"International Journal of Robotics Applications and Technologies","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke; Université du Québec à Chicoutimi","funders":"","keywords":"Ambient intelligence; Activity recognition; Context (archaeology); Computer science; Field (mathematics); Cognition; Data science; Key (lock); Human–computer interaction; Artificial intelligence; Psychology; Computer security","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001943711,0.00009099255,0.0001489726,0.000299184,0.00004773265,0.0001494474,0.0004606819,0.00007634515,0.000002771253],"category_scores_gemma":[0.0001184072,0.0000862317,0.00005089088,0.0001484482,0.00007708555,0.0006551105,0.0001370968,0.0001504337,0.0000120673],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000962943,"about_ca_system_score_gemma":0.00002693781,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001475371,"about_ca_topic_score_gemma":0.00001302962,"domain_scores_codex":[0.9991665,0.00002068263,0.0003015931,0.0001833827,0.0002159323,0.0001119275],"domain_scores_gemma":[0.9988501,0.0002815935,0.0003770021,0.0001279614,0.0003368542,0.00002650269],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00001444013,0.0002465165,0.003002012,0.000007649857,0.00006730057,0.000002672506,0.0000533974,0.00003018337,0.004068218,0.00322752,0.00008435454,0.9891958],"study_design_scores_gemma":[0.009623462,0.001213025,0.2602147,0.001257542,0.0001297457,0.0009343706,0.004095693,0.04669902,0.1265455,0.5180343,0.0294468,0.001805842],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06451368,0.0001466164,0.930451,0.004145388,0.0001384248,0.0004674813,0.00001575552,0.00004366387,0.00007795948],"genre_scores_gemma":[0.9695582,0.0002321077,0.02981862,0.00004337483,0.00002428257,0.000291179,0.000003454543,0.000005036044,0.00002372662],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9873899,"threshold_uncertainty_score":0.3516427,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02617318281638532,"score_gpt":0.2696279258238843,"score_spread":0.243454743007499,"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."}}