{"id":"W4210450135","doi":"10.1109/taes.2022.3145296","title":"A Likelihood Ratio Detector for QTMS Radar and Noise Radar","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Aerospace and Electronic Systems","topic":"Quantum Information and Cryptography","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Defence Research and Development Canada; Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Detector; Radar; Receiver operating characteristic; Lemma (botany); Algorithm; Mathematics; Function (biology); Physics; Computer science; Statistics; Optics; Telecommunications","routes":{"ca_aff":true,"ca_fund":true,"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":[0.005193451,0.00113677,0.001549716,0.00149603,0.0004771531,0.001615593,0.001905649,0.002757978,0.002659717],"category_scores_gemma":[0.02188608,0.0005769644,0.001322087,0.001066986,0.002460383,0.003115079,0.001964247,0.002207326,0.001190828],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001667798,"about_ca_system_score_gemma":0.001399671,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008588016,"about_ca_topic_score_gemma":0.000525191,"domain_scores_codex":[0.9967036,0.001374939,0.0001277261,0.000462669,0.001077867,0.0002531571],"domain_scores_gemma":[0.990854,0.006505042,0.0007918289,0.0007088384,0.0009544897,0.0001856618],"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":[0.0003180049,0.00008546408,0.001830711,0.0002202409,0.0001081157,0.0005554337,0.0002008853,0.3261247,0.01916414,0.5616082,0.00268421,0.08709992],"study_design_scores_gemma":[0.00002838902,0.00008698215,0.000232491,0.00002338899,0.00001943798,0.0004647358,0.00001380822,0.928336,0.005904828,0.06268112,0.002162268,0.00004669684],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004023758,0.0002300502,0.9939442,0.0002163888,0.00003141373,0.00002577909,0.00002279659,0.0001708012,0.001334764],"genre_scores_gemma":[0.3798746,0.0006565375,0.6136204,0.0008008389,0.0002108796,0.0002514271,0.0001863238,0.0002063559,0.004192516],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005193451,"threshold_uncertainty_score":0.02746594,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007015246153559748,"score_gpt":0.206715141761982,"score_spread":0.1996998956084223,"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."}}