{"id":"W4247354999","doi":"10.22541/au.158091285.53307195","title":"Improving Passenger Safety in Cars Using Novel Radar Signal Processing","year":2020,"lang":"en","type":"dataset","venue":"Authorea","topic":"Radar Systems and Signal Processing","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Radar; Computer science; Radar signal processing; Signal processing; Automotive engineering; Transport engineering; Engineering; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003987122,0.0006283205,0.000850104,0.0003075176,0.0001670633,0.0002508491,0.0004393933,0.0005905882,0.00004855938],"category_scores_gemma":[0.00003987195,0.0006472283,0.0001374603,0.0005823447,0.00004840609,0.0003377116,0.0001109101,0.001239333,0.00003873414],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004025585,"about_ca_system_score_gemma":0.0003000439,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009647965,"about_ca_topic_score_gemma":0.0001718597,"domain_scores_codex":[0.9972829,0.00005033806,0.0009033914,0.0006141482,0.0004892392,0.0006600139],"domain_scores_gemma":[0.9991339,0.00006174552,0.000233613,0.0003140238,0.00005586532,0.0002008676],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00004716547,0.00007303407,0.00001835143,0.006954328,0.0001443744,0.0004284168,0.0006607657,0.0186506,0.02858925,0.000009456357,0.8983241,0.04610013],"study_design_scores_gemma":[0.0005720548,0.00002814596,0.00002819132,0.001504904,0.0001230714,0.00008950402,0.0001735725,0.2006623,0.00030013,0.00001526296,0.7954435,0.001059357],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0002519607,0.002511354,0.03372017,0.00003418387,0.000756812,0.0004938169,0.961674,0.0003355443,0.0002221648],"genre_scores_gemma":[0.0542068,0.00008202287,0.008442711,0.0001279631,0.001886737,0.00003552703,0.9348381,0.0003500206,0.00003015976],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1820117,"threshold_uncertainty_score":0.9995979,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02117730222870606,"score_gpt":0.2362810561305871,"score_spread":0.215103753901881,"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."}}