{"id":"W2962902933","doi":"10.1109/thms.2017.2693242","title":"Qualitative Action Recognition by Wireless Radio Signals in Human–Machine Systems","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Human-Machine Systems","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":36,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"National Natural Science Foundation of China","keywords":"Computer science; Quality (philosophy); Action (physics); Key (lock); Artificial neural network; Artificial intelligence; Identification (biology); Variety (cybernetics); SIGNAL (programming language); Wireless; Human–computer interaction; Machine learning; Telecommunications; Computer security","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"],"consensus_categories":[],"category_scores_codex":[0.000719219,0.0004982383,0.0006851104,0.0005927859,0.001135636,0.0003969771,0.0004991653,0.0004087027,0.00006342518],"category_scores_gemma":[0.00001846998,0.0005067625,0.0001511876,0.0002787004,0.0001557738,0.0006694049,0.000003386227,0.0007777548,0.00008532133],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004495139,"about_ca_system_score_gemma":0.00001830535,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004598515,"about_ca_topic_score_gemma":0.001200374,"domain_scores_codex":[0.9973361,0.0003258761,0.0009347252,0.0004886664,0.0004302827,0.0004843507],"domain_scores_gemma":[0.9984829,0.0001626048,0.0003096421,0.0007899262,0.0001494574,0.0001054285],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001782227,0.001006094,0.0002599976,0.003061715,0.0009275137,0.00006965282,0.01048585,0.7844156,0.1726147,0.002052638,0.004494492,0.02043349],"study_design_scores_gemma":[0.01019499,0.001254571,0.0003345385,0.003485619,0.0003466299,0.0001276504,0.0220594,0.656235,0.2969163,0.0009204063,0.003661841,0.004463012],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3215351,0.001127148,0.6655772,0.00005723852,0.003855182,0.001572501,0.001021193,0.001948941,0.003305506],"genre_scores_gemma":[0.9981205,0.0001775689,0.00001870767,0.000008453522,0.0001213184,0.0004640042,0.0001690298,0.0001167713,0.0008036586],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6765854,"threshold_uncertainty_score":0.9997384,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07680294279126616,"score_gpt":0.3505973635846329,"score_spread":0.2737944207933668,"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."}}