{"id":"W2080753128","doi":"10.1109/aire.2014.6894855","title":"A case study of applying data mining to sensor data for contextual requirements analysis","year":2014,"lang":"en","type":"article","venue":"","topic":"Mobile and Web Applications","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Unobservable; Computer science; Context (archaeology); Contextual design; Context model; Requirements engineering; Mobile computing; Data mining; Mobile device; Data science; Context awareness; Artificial intelligence; World Wide Web; Geography","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":[0.005890859,0.0007183542,0.0006563094,0.001776753,0.001497767,0.001611373,0.001482702,0.001710412,0.0006369192],"category_scores_gemma":[0.02079781,0.0004368804,0.0009513915,0.00233525,0.001180681,0.001548294,0.001406934,0.00147625,0.0002574894],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001389605,"about_ca_system_score_gemma":0.001711593,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006400191,"about_ca_topic_score_gemma":0.01276767,"domain_scores_codex":[0.9928383,0.003678518,0.0005612648,0.0007784716,0.001812799,0.0003306353],"domain_scores_gemma":[0.9744912,0.01838141,0.001052017,0.00265695,0.002824537,0.0005939212],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.001521693,0.003076801,0.1389895,0.003308734,0.0007093754,0.0163233,0.02171364,0.2733713,0.06276917,0.01977275,0.008439118,0.4500046],"study_design_scores_gemma":[0.0001920307,0.001390922,0.05136396,0.0004950709,0.0003198561,0.005125015,0.01660468,0.75419,0.108028,0.01296485,0.04908202,0.0002436785],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.692235,0.000486701,0.2955554,0.0020119,0.00008728348,0.001354087,0.001184341,0.0007409537,0.006344376],"genre_scores_gemma":[0.766606,0.000238903,0.2310399,0.0001393003,0.00001908375,0.0003733494,0.000658248,0.0000667288,0.0008585303],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006400191,"threshold_uncertainty_score":0.03115422,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2099301581454019,"score_gpt":0.3889877563059536,"score_spread":0.1790575981605517,"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."}}