{"id":"W4200329580","doi":"10.1145/3494994","title":"IMU2Doppler","year":2021,"lang":"en","type":"article","venue":"Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies","topic":"Advanced SAR Imaging Techniques","field":"Engineering","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Activity recognition; Inertial measurement unit; Domain (mathematical analysis); Adaptation (eye); Context (archaeology); Domain adaptation; Artificial intelligence; Radar; Machine learning; Key (lock); Component (thermodynamics); Baseline (sea); Labeled data; Telecommunications","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":[0.0004882716,0.001955324,0.001122406,0.001201768,0.0006105521,0.001351228,0.001139257,0.001041348,0.02797208],"category_scores_gemma":[0.001520764,0.0004274462,0.0004643228,0.001284253,0.0002936462,0.001265526,0.002037736,0.0011023,0.03367095],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004408575,"about_ca_system_score_gemma":0.000551661,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004013294,"about_ca_topic_score_gemma":0.005057341,"domain_scores_codex":[0.9992884,0.000073441,0.00003100987,0.0002752669,0.0002203511,0.0001115952],"domain_scores_gemma":[0.9996706,0.00002446352,0.00001843452,0.0001318136,0.0001179519,0.000036693],"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":[0.001719165,0.0001584628,0.006803515,0.0007279298,0.000267808,0.0004556724,0.0003047752,0.013314,0.0249922,0.005035243,0.4427541,0.5034672],"study_design_scores_gemma":[0.000221505,0.0003191529,0.0107757,0.0001269443,0.0001038715,0.0005216951,0.0001966144,0.1483888,0.06406638,0.00642023,0.7687119,0.0001471967],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07705717,0.005471702,0.3323354,0.001968468,0.004245279,0.001247082,0.1044034,0.2655067,0.2077648],"genre_scores_gemma":[0.5197651,0.001659361,0.1981039,0.001615688,0.0007287962,0.000998478,0.2063214,0.007637698,0.0631696],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02797208,"threshold_uncertainty_score":0.09357589,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007790591688474904,"score_gpt":0.2391646379259003,"score_spread":0.2313740462374254,"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."}}