{"id":"W4400624347","doi":"10.1016/j.jfranklin.2024.107090","title":"Self-attention CNN based indoor human events detection with UWB radar","year":2024,"lang":"en","type":"article","venue":"Journal of the Franklin Institute","topic":"Advanced SAR Imaging Techniques","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"","keywords":"Radar; Computer science; Remote sensing; Artificial intelligence; Geography; 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":[0.0002364596,0.0005487099,0.000510471,0.0005057125,0.0001332678,0.0003716856,0.0005994567,0.0004657379,0.001328869],"category_scores_gemma":[0.0004302024,0.0002749164,0.0004071688,0.0003934208,0.0001495782,0.0004427603,0.0005869748,0.0004189598,0.000726292],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002076765,"about_ca_system_score_gemma":0.0002385001,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00198487,"about_ca_topic_score_gemma":0.004315379,"domain_scores_codex":[0.9998114,0.00001954885,0.000005781264,0.00006970766,0.0000444628,0.00004906919],"domain_scores_gemma":[0.9998684,0.00003569038,0.00001624908,0.00002522269,0.00004269168,0.00001178001],"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":[0.0004834287,0.0002499652,0.006988682,0.0001145779,0.0001690154,0.0003907133,0.00009705592,0.06222714,0.0992122,0.00144479,0.007010846,0.8216115],"study_design_scores_gemma":[0.000008059342,0.0001162748,0.008455287,0.00001064965,0.0000744459,0.0003207452,0.00003417658,0.9600459,0.0276956,0.001125462,0.002096852,0.00001643012],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3106236,0.001572268,0.6724719,0.0003719755,0.00045606,0.00005635498,0.0005737347,0.002954602,0.01091949],"genre_scores_gemma":[0.9301077,0.0004939121,0.05929882,0.0002693538,0.0001501698,0.00002035371,0.0006958641,0.0000836048,0.008880275],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00198487,"threshold_uncertainty_score":0.004445493,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006892020874696017,"score_gpt":0.2350494700520398,"score_spread":0.2281574491773438,"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."}}