{"id":"W2558455691","doi":"10.1109/iemcon.2016.7746307","title":"A Context-aware Recommendation System using smartphone sensors","year":2016,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Global Positioning System; Mobile device; Android (operating system); Geospatial analysis; Context (archaeology); Recommender system; Mobile computing; Ubiquitous computing; World Wide Web; Context awareness; Human–computer interaction; Multimedia; Telecommunications; Remote sensing","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0003790379,0.0008296841,0.001186033,0.001150665,0.0007294053,0.0009409595,0.001109932,0.001138372,0.003458243],"category_scores_gemma":[0.0007886006,0.0004750648,0.000548388,0.0008569628,0.0001354802,0.0009867102,0.0006231519,0.000616368,0.002154637],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004363456,"about_ca_system_score_gemma":0.0005405641,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01678003,"about_ca_topic_score_gemma":0.01995844,"domain_scores_codex":[0.9995741,0.00003835742,0.00005473756,0.0001544806,0.0001258484,0.00005245793],"domain_scores_gemma":[0.9995002,0.00006537163,0.00003554181,0.00008222408,0.0002524112,0.00006441199],"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":[0.001937865,0.0008277072,0.02122067,0.0007738619,0.0004975158,0.002354859,0.000562894,0.01535373,0.1777302,0.002323009,0.03179252,0.7446252],"study_design_scores_gemma":[0.0005564668,0.001925036,0.04941659,0.0002563685,0.00111754,0.00339065,0.0006850794,0.7044175,0.1605874,0.002188236,0.07497505,0.0004840838],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3446253,0.006886446,0.5552761,0.001463776,0.00113409,0.001447728,0.003317307,0.05532423,0.03052497],"genre_scores_gemma":[0.813604,0.001406563,0.1658391,0.0005488337,0.0001888367,0.0003905203,0.00131034,0.0001352644,0.01657654],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01678003,"threshold_uncertainty_score":0.03336471,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04512569050612207,"score_gpt":0.2556580467710841,"score_spread":0.210532356264962,"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."}}