{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000194808,0.0004057723,0.000437133,0.0002056512,0.0001620588,0.0001177428,0.0007888323,0.0003199254,0.00007690984],"category_scores_gemma":[0.000009393608,0.0004254711,0.0001978469,0.0002299888,0.00006862283,0.0002566286,0.0004844971,0.0005827924,0.0001463719],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003775871,"about_ca_system_score_gemma":0.00004772231,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000936893,"about_ca_topic_score_gemma":0.00003556094,"domain_scores_codex":[0.9983293,0.00002461303,0.0002804915,0.0007822395,0.00009521576,0.0004881847],"domain_scores_gemma":[0.9988865,0.00004964456,0.0001314574,0.0007069817,0.0000491873,0.000176211],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006038267,0.00004628069,0.00618719,0.002272754,0.0005527291,0.0009102208,0.0005204541,0.9555031,0.00617627,0.02547669,0.0006384975,0.001655441],"study_design_scores_gemma":[0.0007718192,0.0000318109,0.0004786747,0.001945934,0.0001521836,0.00002672045,0.0001845701,0.914838,0.002654132,0.07574499,0.001486441,0.001684741],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3615415,0.0004419937,0.6159127,0.00002349458,0.001339784,0.0002263466,0.00001934129,0.0007897104,0.01970506],"genre_scores_gemma":[0.9977456,0.0001159687,0.0002158462,0.00001240693,0.0004132604,7.155066e-7,0.000007772201,0.00007692473,0.001411531],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6362041,"threshold_uncertainty_score":0.9998197,"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."}}