{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006298747,0.0004472391,0.0004125489,0.0006984526,0.0001852019,0.0007517558,0.0004152863,0.0005145005,0.0008394247],"category_scores_gemma":[0.002878794,0.0002036921,0.0002891929,0.0005290421,0.0009477743,0.0009701743,0.0005681127,0.0004216036,0.0002030108],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004630406,"about_ca_system_score_gemma":0.0001908186,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00228922,"about_ca_topic_score_gemma":0.001208259,"domain_scores_codex":[0.9995331,0.0001593222,0.00002613883,0.0001209813,0.0001149326,0.00004552376],"domain_scores_gemma":[0.9992056,0.0004455039,0.0001517887,0.00006490629,0.00009590643,0.00003636228],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0005048673,0.0001948708,0.02318198,0.0004693416,0.0001890578,0.000594335,0.0008995662,0.5262286,0.08860416,0.03002872,0.001771713,0.3273328],"study_design_scores_gemma":[0.000007993165,0.0001209634,0.01486763,0.00001570961,0.00002022999,0.0001105603,0.0001174318,0.9618718,0.008078538,0.01389297,0.0008655796,0.00003048238],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2539406,0.0009844687,0.7409577,0.0003182283,0.00008342967,0.00005598196,0.0001649647,0.0006755596,0.00281897],"genre_scores_gemma":[0.9756578,0.0001648602,0.02331346,0.00004687578,0.00002492231,0.00002009481,0.00005541809,0.00001654524,0.0007000935],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00228922,"threshold_uncertainty_score":0.004551828,"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."}}