{"id":"W2955606553","doi":"10.48550/arxiv.1611.06443","title":"Spectrum Sharing Radar: Coexistence via Xampling","year":2016,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Radar Systems and Signal Processing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Azrieli Foundation; European Commission","keywords":"Cognitive radio; Radar; Interference (communication); Computer science; Spectrum (functional analysis); Process gain; Telecommunications; Electronic engineering; Real-time computing; Spread spectrum; Engineering; Wireless; Physics; Channel (broadcasting)","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001213552,0.0004765538,0.0006671899,0.0005115445,0.0007717464,0.001572843,0.001616285,0.001179344,0.002070621],"category_scores_gemma":[0.003034937,0.000279682,0.0004213469,0.0004374791,0.001598618,0.002772182,0.003117121,0.001583348,0.0006455545],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002819733,"about_ca_system_score_gemma":0.0003776132,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002138739,"about_ca_topic_score_gemma":0.0001885041,"domain_scores_codex":[0.9985752,0.000400212,0.00005904789,0.0003561354,0.000428391,0.0001809414],"domain_scores_gemma":[0.9983911,0.0007136103,0.0001976837,0.000374917,0.0001950023,0.0001276711],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000733114,0.0003200456,0.001386202,0.000354233,0.0001337167,0.00103363,0.0009975076,0.08390845,0.1539984,0.4198167,0.002409658,0.3349083],"study_design_scores_gemma":[0.0001210597,0.0008953949,0.0007039938,0.00007433143,0.00008855441,0.002404782,0.0002600072,0.7347527,0.05268247,0.1880967,0.01980061,0.0001193909],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03772369,0.0006253786,0.949128,0.0003123679,0.0001218079,0.0000461576,0.000009979931,0.0003491371,0.01168351],"genre_scores_gemma":[0.825793,0.0005212238,0.167349,0.0004761314,0.0002649936,0.00009561812,0.000029047,0.00005904997,0.005411898],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002070621,"threshold_uncertainty_score":0.006926954,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05891838434700271,"score_gpt":0.1745833318496685,"score_spread":0.1156649475026658,"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."}}