{"id":"W986746244","doi":"","title":"Cognitive assistance to meal preparation : design, implementation, and assessment in a living lab","year":2015,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Trois-Rivières; Université du Québec à Chicoutimi; Group for Research in Decision Analysis; Université de Montréal; Université de Sherbrooke","funders":"","keywords":"Autonomy; Assistive technology; Living lab; Assisted living; Independent living; Cognition; Ambient intelligence; Activities of daily living; Meal preparation; Computer science; Quality of life (healthcare); Unit (ring theory); Psychology; Knowledge management; Applied psychology; Human–computer interaction; Gerontology; Medicine","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":[0.0117667,0.0003458656,0.000447864,0.0004001661,0.0002185874,0.001114772,0.001017191,0.0001896551,0.00003197276],"category_scores_gemma":[0.001233041,0.0004093502,0.00007730255,0.0006416286,0.00007426544,0.0006498539,0.002253997,0.0005626277,0.00002434728],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004725796,"about_ca_system_score_gemma":0.001114529,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001854835,"about_ca_topic_score_gemma":0.01315468,"domain_scores_codex":[0.9879902,0.009090751,0.0007215161,0.001178422,0.0006457898,0.0003733291],"domain_scores_gemma":[0.9916443,0.00295964,0.0006064198,0.001234992,0.003287642,0.0002669621],"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":[0.00007643151,0.001741117,0.02961197,0.0004869015,0.0003192748,0.00003488921,0.1777504,0.0003381533,0.00265541,0.05354553,0.004752524,0.7286874],"study_design_scores_gemma":[0.008185062,0.00003315004,0.3574774,0.03281515,0.0002942779,0.0001723764,0.01428348,0.4717174,0.05368577,0.03855005,0.01615408,0.00663185],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07285234,0.0003154,0.9129969,0.004880606,0.000207811,0.001423628,0.00004787271,0.0002045481,0.007070848],"genre_scores_gemma":[0.88385,0.000064798,0.1146704,0.0001325677,0.0000181702,0.0005098125,0.00009047598,0.00002510927,0.0006386823],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8109976,"threshold_uncertainty_score":0.9999222,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04760307705661504,"score_gpt":0.3274847023913853,"score_spread":0.2798816253347703,"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."}}