{"id":"W2754432013","doi":"10.1145/3130906","title":"Rapid","year":2017,"lang":"en","type":"article","venue":"Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":55,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Computer science; Identification (biology); Noise (video); Fuse (electrical); Key (lock); Channel (broadcasting); Real-time computing; Gait; Artificial intelligence; Telecommunications; Engineering; Computer security; Electrical 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":[0.0008704189,0.001190573,0.0007114751,0.00114328,0.0005858157,0.001557472,0.001970534,0.001067253,0.109859],"category_scores_gemma":[0.002568873,0.0004603351,0.000543913,0.0007201882,0.0003172692,0.002392965,0.002394476,0.0007795952,0.09251197],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004438738,"about_ca_system_score_gemma":0.0009015666,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001216415,"about_ca_topic_score_gemma":0.001732691,"domain_scores_codex":[0.999092,0.0001162012,0.00005692349,0.000262567,0.0003581009,0.0001141399],"domain_scores_gemma":[0.9986522,0.0001822133,0.0000858958,0.0004547876,0.0005089234,0.0001159422],"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":[0.001260499,0.0002238141,0.002775411,0.001035228,0.00009915705,0.0005701786,0.0003416508,0.00331891,0.03740991,0.01578626,0.3905333,0.5466456],"study_design_scores_gemma":[0.0002011473,0.0004845345,0.00248749,0.0001440049,0.000072702,0.001415507,0.0001767141,0.03544238,0.03527094,0.006822033,0.9173391,0.0001435482],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02743379,0.002444639,0.3940958,0.001270014,0.002049444,0.00148892,0.01993275,0.2786612,0.2726234],"genre_scores_gemma":[0.2711394,0.002165378,0.289524,0.002778956,0.0007233349,0.001828353,0.05115543,0.01269167,0.3679936],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.109859,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0113285649075886,"score_gpt":0.2364010572457525,"score_spread":0.2250724923381639,"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."}}