{"id":"W1809001740","doi":"10.1007/11853565_13","title":"Mobility Detection Using Everyday GSM Traces","year":2006,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":277,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"National Science Foundation","keywords":"GSM; Computer science; Mobile phone; Tower; Mobile telephony; Phone; Everyday life; Activity recognition; Real-time computing; Telecommunications; Artificial intelligence; Mobile radio; Engineering","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":[0.0001274922,0.0006921198,0.0004479862,0.001945987,0.0001844693,0.0005262167,0.0004525375,0.0005555731,0.001599848],"category_scores_gemma":[0.0007554812,0.0001845096,0.000267256,0.001105254,0.0001416274,0.0004608452,0.0004859658,0.000221731,0.001619075],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001137053,"about_ca_system_score_gemma":0.000162363,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001831257,"about_ca_topic_score_gemma":0.003354646,"domain_scores_codex":[0.9998583,0.00002458455,0.000008539977,0.00003693623,0.00003879816,0.00003276513],"domain_scores_gemma":[0.999723,0.0000863429,0.00003231029,0.00003459387,0.0000879626,0.00003580153],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001126942,0.0002651324,0.1146882,0.000466879,0.0002178284,0.001277634,0.0004980695,0.02332235,0.1426323,0.001425536,0.005019738,0.7090594],"study_design_scores_gemma":[0.00009287355,0.0008335629,0.1794924,0.0001760349,0.0002907394,0.005308349,0.001075814,0.6959379,0.1001507,0.004474047,0.01204987,0.0001177692],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.7280701,0.0006907018,0.2571957,0.0001168224,0.0001229186,0.00009759541,0.002408748,0.004427831,0.00686956],"genre_scores_gemma":[0.9596635,0.0003793323,0.03592424,0.00002957024,0.00004161991,0.00003167653,0.001533669,0.00007427169,0.002322168],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.001945987,"threshold_uncertainty_score":0.00535202,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03051327408169161,"score_gpt":0.2563367977946298,"score_spread":0.2258235237129382,"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."}}