{"id":"W2743750036","doi":"10.1007/978-3-319-66188-9_9","title":"Activity Model for Interactive Micro Context-Aware Well-Being Applications Based on ContextAA","year":2017,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Ontic; Computer science; Semantics (computer science); Context (archaeology); Adaptation (eye); Representation (politics); Human–computer interaction; Smart environment; Ubiquitous computing; Ambient intelligence; Programming language; World Wide Web; Internet of Things","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":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.001116437,0.0009044791,0.001059119,0.001081072,0.001000655,0.001357405,0.004626801,0.0005265829,0.00001333381],"category_scores_gemma":[0.0001621,0.0009096,0.0004325237,0.000228056,0.0007602674,0.001458585,0.0009663309,0.001204309,0.0001015948],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008417277,"about_ca_system_score_gemma":0.001223867,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005823042,"about_ca_topic_score_gemma":0.0003596414,"domain_scores_codex":[0.9947722,0.00008103655,0.0006464353,0.002666641,0.0009752362,0.0008584555],"domain_scores_gemma":[0.9921504,0.002730729,0.001050188,0.002891365,0.0008932208,0.0002840509],"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":[0.00006738918,0.0001188012,0.00001701234,0.00008722702,0.00003336841,0.00001651122,0.0006296415,0.01556135,0.0003123375,0.003845099,0.0000672389,0.979244],"study_design_scores_gemma":[0.0008355911,0.0001763942,0.00002605591,0.000836492,0.00001886742,0.00002486764,5.266257e-7,0.9488023,0.003953601,0.04049513,0.003914277,0.0009158991],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00002985309,0.00005353876,0.988307,0.001522818,0.001145097,0.002648311,0.00008397963,0.0002775879,0.005931787],"genre_scores_gemma":[0.9370964,0.000006227113,0.05784337,0.002676322,0.0004555449,0.0004417457,0.00001963204,0.00007920235,0.001381545],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9783281,"threshold_uncertainty_score":0.9996793,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03145519169797074,"score_gpt":0.287416315246449,"score_spread":0.2559611235484782,"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."}}